Genetic and Evolutionary Computation Conference 2021

Conference Program

Last updated: July 20, 2021

Lille, France July 10-14, 2021

Page

Welcome 3 Sponsors and Supporters 4 Organizers and Track Chairs 5 Program Committee 7 Time Zone 20 Guide for Attendees 21 Virtual Venue 24 Schedule 27 Schedule at a Glance 28 Workshop and Tutorial Sessions 29 Paper Sessions Overview 31 Track List and Abbreviations 32 Keynotes 33 Tutorials 37 Workshops, Late-breaking Abstracts, and Women@GECCO 43 Workshops 44 Late-breaking Abstracts 56 Women@GECCO 57 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 59 Annual “Humies” Awards for Human-Competitive Results 60 Competitions 61 Evolutionary Computation in Practice 70 Hot Off the Press 71 Job Market 72 SIGEVO Summer School 73 Best Paper Nominations 75 Voting Instructions 76 Papers and Posters 79 Monday, July 12, 10:20-11:40 80 Monday, July 12, 14:20-15:40 81 Tuesday, July 13, 12:20-13:40 84 Tuesday, July 13, 14:20-15:40 85 Wednesday, July 14, 10:20-11:40 88 Wednesday, July 14, 12:20-13:40 91 Poster Session 93 Abstracts by Track 103 ACO-SI 104 CS 106 ECOM 109 EML 112 EMO 115 ENUM 118 GA 119 GECH 122 GP 125 HOP 128 NE 133 RWA 135 SBSE 140 THEORY 141 Author Index 145 2

GECCO is sponsored by the Association for Computing Machinery Special Interest Group for Genetic and Evolutionary Computation (SIGEVO). SIG Services: 2 Penn Plaza, Suite 701, New York, NY, 10121, USA, 1-800-342-6626 (USA and Canada) or +212-626-0500 (global). Welcome 3

Welcome Dear GECCO attendees, Welcome to the 2021 Genetic and Evolutionary Computation Conference (GECCO), hosted from Lille, France. GECCO’21 is the second edition, after GECCO’20 in Cancún, México, to be held online due to the global coronavirus crisis. For most of 2020, the Organizing Committee was making preparations for a hybrid mode. However, as progress at fighting the pandemic was slower than we had hoped, in December 2020 the decision was made to go fully online. GECCO is the premier peer-reviewed conference in the field of Evolutionary Computation, and the main conference of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM). To identify the most important and technically sound papers, the conference implements a rigorous and selective review process, conducted by two chairs per track in coordination with the Editor-in-Chief. The technical program is divided into 13 tracks reflecting all aspects of our field, including the new Neuroevolution track. This year, we received 362 regular paper submissions and accepted 134 of them as oral presentations (37% accep- tance rate) and 136 as posters. Besides the technical tracks, GECCO’21 offers 38 tutorials selected from 45 proposals, 22 workshops that cover important topics in our field, and a range of events: the Humies Awards ceremony, 13 com- petitions, Women@GECCO, Evolutionary Computation in Practice, a five-day SIGEVO Summer School preceding the conference, and more. The highlights of the event are the keynotes given by three esteemed researchers: Joshua Tenenbaum of MIT, USA, Marc Mézard of École Normale Supérieure, France, and Melanie Mitchell of Santa Fe Institute, USA (SIGEVO keynote). We are thankful to all authors, tutorial speakers, as well as workshop and competition organizers who contributed to GECCO despite the precarious pandemic conditions. We would also like to express our thanks to all organizers, in particular to all chairs: tracks, tutorials, workshops, publicity, competitions, late breaking abstracts, and hot-off- the-press. We also thank the organizers of the Humies, and of Women@GECCO, as well as to the members of our Program Committee. We sincerely appreciate all these efforts and contributions. Some members of the organization team deserve particular recognition: Francisco Chicano, Editor-in-Chief; Bilel Derbel, Local Chair; Nadarajen Veerapen, Electronic Media Chair; Arnaud Liefooghe, Virtualization Chair; Aniko Ekart, Publicity Chair, and Alberto Tonda, Proceedings Chair. The effort of this core organizing team was essential for the success of GECCO’21. We also thank Ahmed Kheiri for optimizing the schedule of the conference, Brenda Ramirez and Roxane Rose of Executive Events who helped us with registrations and the logistics of the event, as well as Franz Rothlauf, Enrique Alba, Darrell Whitley and Peter Bosman from SIGEVO and the Business Committee for their valuable advice and guidance. Last but not least, our gratitude goes to our generous business sponsors and institutional supporters: Cognizant, Google, eMapa, as well as Université de Lille and Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL). The Organizing Committee worked hard to provide diversified means for participating remotely in GECCO’21. To this aim, we engaged a few interlinked online platforms, which should facilitate not only listening to talks, but also more informal interaction in coffee breaks, poster sessions, and other events. We hope these arrangements will translate into a great experience for all attendees. Enjoy the conference!

Krzysztof Krawiec GECCO 2021 General Chair Poznan University of Technology Pozna´n,Poland 4 Sponsors and Supporters

Sponsors and Supporters

We gratefully acknowledge and thank our sponsors:

Gold sponsor

Silver sponsor

Bronze sponsor

Supporters: Organizers and Track Chairs 5

Organizers

General Chair: Krzysztof Krawiec, Poznan University of Technology, Center for Artificial Intelligence and Machine Learning

Editor-in-Chief: Francisco Chicano, University of Malaga

Local Chair: Bilel Derbel, University of Lille

Proceedings Chair: Alberto Tonda, National Institute of Research for Agriculture, Food, and the Environment (INRAE), Université Paris-Saclay

Student Affairs Chairs: Katya Rodríguez-Vázquez, IIMAS-UNAM Esara Tari, Université Littoral Côte d’Opale

Publicity Chair: Aniko Ekart, Aston University

Electronic Media Chair: Nadarajen Veerapen, University of Lille

Virtualization Chair: Arnaud Liefooghe, University of Lille

Tutorials Chair: Gisele L. Pappa, Federal University of Minas Gerais – UFMG

Workshops Chairs: Alberto Moraglio, University of Exeter Hugo Terashima-Marín, Tecnológico de Monterrey

Competitions Chairs: Marcella Scoczynski Ribeiro Martins, Federal University of Technology (UTFPR) Markus Wagner, School of Computer Science, The University of Adelaide

Evolutionary Computation in Thomas Bartz-Beielstein, TH Köln Practice chairs: Bogdan Filipic, Jožef Stefan Institute Sowmya Chandrasekaran, TH Köln

Hot off The Press Chair: Carola Doerr, CNRS, Sorbonne University

Late-breaking Abstracts Chair: Federica Sarro, University College London

Annual "Humies Awards for John Koza, Standford University Human Competitive Results" Erik Goodman, University of Michigan chairs: and BEACON Center for the Study of Evolution in Action William B. Langdon, University College London

Summer School Organizers: Christine Zarges, Aberystwyth University Miguel Nicolau, University College Dublin

Women @ GECCO: Marie-Eléonore Kessaci, University of Lille Swetha Varadarajan, Colorado State University

Job Market: Tea Tušar, Jožef Stefan Institute Boris Naujoks, Cologne University of Applied Sciences

Business Committee: Peter A.N. Bosman, Centre for Mathematics and Computer Science, Delft University of Technology Darrell Whitley, Colorado State University 6 Organizers and Track Chairs

Track Chairs

ACO-SI — Ant Colony Optimization and Swarm Intelligence Marde Helbig, Griffith University Christopher Cleghorn, University of the Witwatersrand

CS — Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware) Dennis G. Wilson, ISAE-SUPAERO, University of Toulouse Georgios N. Yannakakis, Institute of Digital Games, University of Malta

ECOM — Evolutionary Combinatorial Optimization and Metaheuristics Luís Paquete, University of Coimbra Gabriela Ochoa, University of Stirling

EML — Evolutionary Machine Learning Jaume Bacardit, Newcastle University Christian Gagné, Université Laval

EMO — Evolutionary Multiobjective Optimization Sanaz Mostaghim, University of Magdeburg Laetitia Jourdan, University of Lille

ENUM — Evolutionary Numerical Optimization Oliver Schuetze, CINVESTAV-IPN Petr Pošík, Czech Technical University in Prague

GA — Genetic Algorithms Carlos Segura, Centro de Investigación en Matemáticas (CIMAT) Renato Tinós, University of São Paulo

GECH — General Evolutionary Computation and Hybrids Carlos Cotta, University of Málaga Malcolm Heywood, Dalhousie University

GP — Genetic Programming Mengjie Zhang, Victoria University of Wellington Leonardo Trujillo, Tecnológico Nacional de México/Instituto Tecnologico de Tijuana

NE — Neuroevolution Risto Miikkulainen, The University of Texas at Austin Bing Xue, Victoria University of Wellington

RWA — Real World Applications Aneta Neumann, The University of Adelaide Richard Allmendinger, University of Manchester

SBSE — Search-Based Software Engineering Fuyuki Ishikawa, National Institute of Informatics Inmaculada Medina-Bulo, Universidad de Cádiz

Theory Frank Neumann, The University of Adelaide Andrew M. Sutton, University of Minnesota Duluth Program Committee 7

Program Committee (Proceedings)

Adair, Jason, University of Stirling Birattari, Mauro, Université Libre de Bruxelles Adekoya, Adekunle Rotimi, Stellenbosch University Blank, Julian, Michigan State University Affenzeller, Michael, University of Applied Science Upper Blesa, Maria J., Universitat Politècnica de Catalunya - Austria BarcelonaTech Aguirre, Hernan, Shinshu University Blot, Aymeric, University College London Akimoto, Youhei, University of Tsukuba Booker, Lashon, The MITRE Corporation Al-Helali, Baligh, Victoria University of Wellington Bosman, Peter A.N., Centre for Mathematics and Computer al-Rifaie, Mohammad Majid, University of Greenwich Science Al-Sahaf, Harith, Victoria University of Wellington Bossek, Jakob, University of Münster Alaya, Ines, Ecole Nationale des Sciences de l’Informatique Boumaza, Amine, Université de Lorraine / LORIA (ENSI) Bouter, Anton, CWI Albukhanajer, Wissam, Roke Manor Research Limited Bredeche, Nicolas, Sorbonne Université Alderliesten, Tanja, Leiden University Medical Center Brockhoff, Dimo, INRIA Saclay - Ile-de-France Alexander, Bradley James, University of Adelaide Browne, Will Neil, Victoria University of Wellington Ali, Shaukat, Simula Research Laboratory Brownlee, Alexander, University of Stirling Allmendinger, Richard, University of Manchester Bruce, Bobby R., UC Davis Amaya, Jhon Edgar, UNET Bull, Larry, University of the West of England Antipov, Denis, ITMO University Butz, Martin V., University of Tübingen Antunes, Carlos, DEEC-UC Buzdalov, Maxim, ITMO University Arcaini, Paolo, National Institute of Informatics Buzdalova, Arina, ITMO University Arias Montaño, Alfredo, IPN-ESIME Byrski, Aleksander, AGH University of Science and Technol- Arita, Takaya, Nagoya University ogy Arnaldo, Ignacio, PatternEx Bäck, Thomas, Leiden University Arnold, Dirk, Dalhousie University Cagnoni, Stefano, University of Parma Assimi, Hirad, University of Adelaide Camacho, David, Universidad Politécnica de Madrid Auger, Anne, INRIA Candadai, Madhavun, Indiana University Aydın, Dogan, Dumlupınar University Capodieci, Nicola, university of modena and reggio emilia Aydin, Mehmet Emin, University of the West of England Carmelo J A, Bastos Filho, University of Pernambuco, Po- Azad, Raja Muhammad Atif, Birmingham City University litechnic School of Pernambuco Bagheri, Samineh, TH Köln - University of Applied Sciences Castelli, Mauro, NOVA IMS, Universidade Nova de Lisboa Baioletti, Marco, University of Perugia Castillo, Pedro, UGR Banzhaf, Wolfgang, Michigan State University Cauwet, Marie-Liesse, ESIEE Paris Barbosa, Helio J.C., LNCC Ceberio, Josu, University of the Basque Country Barros, Gabriella, New York University Ceschia, Sara, University of Udine Barros, Marcio, UNIRIO Chacón Castillo, Joel, CIMAT Basseur, Matthieu, Université d’Angers Chakraborty, Uday, University of Missouri Basterrech, Sebastian, VSB-Technical University of Ostrava Chelly Dagdia, Zaineb, Versailles Saint-Quentin-en-Yvelines Basto-Fernandes, Vitor, University Institute of Lisbon University, Paris Saclay Batista, Lucas, Universidade Federal de Minas Gerais Chen, Boyuan, Columbia University Bechikh, Slim, University of Tunis Chen, Gang, Victoria University of Wellington Belavkin, Roman, Middlesex University London Chen, Qi, Victoria University of Wellington Belkhir, Nacim, Safran Chen, Wei-Neng, South China University of Technology Ben Romdhane, Hajer, Institut Supérieur de Gestion de Tunis Chen, Ying-ping, National Chiao Tung University Bentley, Peter J., University College London Cheng, Ran, Southern University of Science and Technology Bernardino, Heder, Universidade Federal de Juiz de Fora Chlebik, Miroslav, University of Sussex (UFJF) Chotard, Alexandre Adrien, KTH Beyer, Hans-Georg, Vorarlberg University of Applied Sciences Chugh, Tinkle, University of Exeter Bezerra, Leonardo, IMD, Universidade Federal do Rio Grande Chávez, Francisco, University of Extremadura do Norte Clemente, Eddie, CICESE Bi, Ying, Victoria University of Wellington Coello Coello, Carlos A., CINVESTAV-IPN Biedrzycki, Rafał, Institute of Computer Science, Warsaw Colanzi, Thelma Elita, Universidade Estadual de Maringá University of Technology Colomine, Feijoo, UNET Bingham, Garrett, The University of Texas at Austin Coninx, Alex, Sorbonne Universite 8 Program Committee

Correia, João, Center for Informatics and Systems of the Uni- Iasi versity of Coimbra Fernandes, Carlos, ISR Costa, Ernesto, University of Coimbra Ferrante, Eliseo, VU University Amsterdam Cuate, Oliver, ESFM-IPN Ferrer, Javier, University of Malaga Cui, Xiaodong, IBM T. J. Watson Research Center Festa, Paola, Universitá degli Studi di Napoli Cully, Antoine, Imperial College Fieldsend, Jonathan Edward, University of Exeter Cussat-Blanc, Sylvain, University of Toulouse Finck, Steffen, FH Vorarlberg University of Applied Sciences Dang, Duc-Cuong, University of Southampton Fischbach, Andreas, TH Köln Danoy, Gregoire, University of Luxembourg Folino, Gianluigi, ICAR-CNR Datta, Dilip, Tezpur University Fontaine, Matthew, University of Southern California De Jong, Kenneth, George Mason University Franzin, Alberto, Université Libre de Bruxelles de la Fraga, Luis Gerardo, CINVESTAV Freitas, Alex A., University of Kent De Lorenzo, Andrea, DIA - University of Trieste Galeotti, Juan Pablo, University of Buenos Aires de Oliveira Neto, Francisco Gomes, Chalmers and the Univer- Gallagher, Marcus R., University of Queensland sity of Gothenburg Galvan, Edgar, Maynooth University Deb, Kalyanmoy, Michigan State University Gao, Xiaoying, Victoria University of Wellington Delgado, Myriam, Federal University of Technology – Paraná Garcia-Nieto, Jose, University of Malaga Delgado-Pérez, Pedro, Universidad de Cádiz García-Martínez, Carlos, Univ. of Córdoba Della Cioppa, Antonio, Natural Computation Lab - DIEM, Gay, Gregory, University of South Carolina University of Salerno Gelareh, Shahin, University of Artois/ LGI2A Derbel, Bilel, Univ. Lille Gentile, Lorenzo, TH Köln - University of Applied Sciences Desell, Travis, Rochester Institute of Technology Giacobini, Mario, University of Torino Deutz, Andre, Leiden University Giavitto, Jean-Louis, CNRS - IRCAM, Sorbonne Université Dick, Grant, University of Otago Giustolisi, Orazio, Technical University of Bari Divina, Federico, Pablo de Olavide University Glasmachers, Tobias, Ruhr-University Bochum Do, Anh, The University of Adelaide Glette, Kyrre, University of Oslo Dockhorn, Alexander, Queen Mary University of London Goeffon, Adrien, LERIA, University of Angers Doerner, Karl F., University of Vienna González de Prado Salas, Pablo, Foqum Doerr, Benjamin, Ecole Polytechnique Gonçalves, Ivo, INESC Coimbra, DEEC, University of Coim- Doerr, Carola, CNRS bra Dolson, Emily L., Michigan State University Goodman, Erik, Michigan State University Doncieux, Stéphane, Sorbonne Universite Graff, Mario, Universidad Michoacana de San Nicolas de Dorin, Alan, Monash University Hidalgo Dorronsoro, Bernabe, University of Cadiz Gravina, Daniele, Institute of Digital Games, University of Dovgan, Erik, Jožef Stefan Institute Malta Dreo, Johann, Thales Research & Technology Greiner, David, Universidad de Las Palmas de Gran Canaria Drezewski, Rafal, AGH University of Science and Technology Grimme, Christian, Münster University Drugan, Madalina, ITLearns.Online Groleaz, Lucas, LIRIS Durillo, Juan, Leibniz Supercomputing Center of the Bavarian Guerreiro, Andreia P., INESC-ID Academy of Science and Humanities Guizzo, Giovani, University College London Duro, Joao, The University of Sheffield Ha, David, Google Brain Duro, Richard, Universidade da Coruña Haddow, Pauline Catriona, NTNU Ebner, Marc, Ernst-Moritz-Universität Greifswald, Germany Hakanen, Jussi, University of Jyvaskyla Ekart, Aniko, Aston University Hamann, Heiko, Institute of Computer Engineering, Univer- El-Abd, Mohammed, American University of Kuwait sity of Lübeck ElSaid, AbdElRahman, Rochester Institute of Technology Hancer, Emrah, Mehmet Akif Ersoy Üniversitesity Engelbrecht, Andries P., Stellenbosch University Handa, Hisashi, Kindai University Eremeev, Anton V., Omsk Branch of Sobolev Institute of Math- Handl, Julia, Mrs ematics Hao, Jin-Kao, University of Angers - LERIA Ernst, Andreas, Monash University Harrison, Kyle Robert, The University of New South Wales Escalante, Hugo, Instituto Nacional de Astrofisica, Optica y Hart, Emma, Edinburgh Napier University Electronica, Mexico He, Cheng, Southern University of Science and Technology Everson, Richard, University of Exeter He, Jun, University of Wales, Aberystwyth Feng, Liang, Chongqing University Helbig, Mardé, Griffith University Ferariu, Lavinia, "Gheorghe Asachi" Technical University of Hellwig, Michael, Vorarlberg University of Applied Sciences Program Committee 9

Helmuth, Thomas, Hamilton College LaTorre, Antonio, Universidad Politécnica de Madrid Hemberg, Erik, MIT CSAIL Lara-Cabrera, Raul, Universidad Politecnica de Madrid Hendtlass, Tim, Swinburne University of Technology Lardeux, Frédéric, University of Angers, France Hernandez-Riveros, Jesús-Antonio, Universidad Nacional de Legrand, Pierrick, Université de Bordeaux Colombia Lehman, Joel, Uber AI Labs Hernando, Leticia, University of the Basque Country Lehre, Per Kristian, University of Birmingham Hernández-Aguirre, Arturo, Centre for Research in Mathe- Lengler, Johannes, ETH Zürich matics Lensen, Andrew, Victoria University of Wellington Heywood, Malcolm, Dalhousie University Li, Bin, University of Science and Technology of China Hintze, Arend, Dalarna University Li, Miqing, University of Birmingham Holena, Martin, Institute of Computer Science Li, Xiangtao, Northeast Normal University Hoover, Amy K., New Jersey Institute of Technology Li, Xiaodong, RMIT University Howard, Gerard, CSIRO Liapis, Antonios, University of Malta Hu, Ting, Queen’s University Liefooghe, Arnaud, Univ. Lille Huizinga, Joost, Uber Technologies Inc. Limmer, Steffen, Honda Research Institute Husbands, Phil, Sussex University Liu, Hai-Lin, Guangdong University of Technology Hähner, Jörg, University of Augsburg Liu, Jialin, Southern University of Science and Technology Iacca, Giovanni, University of Trento Liu, Yiping, Hunan University Ikegami, Takashi, University of Tokyo Lo Bosco, Giosuè, Università degli studi di Palermo Inden, Benjamin, Nottingham Trent University Lobo, Daniel, University of Maryland, Baltimore County Iori, Manuel, University of Modena and Reggio Emilia Lobo, Fernando G., University of Algarve Irurozki, Ekhine, Telecom Paris Lobo Pappa, Gisele, Federal University of Minas Gerais - Ishibuchi, Hisao, Osaka Prefecture University UFMG Izquierdo, Eduardo, Indiana University Loginov, Alexander, Dalhousie University Jakobovic, Domagoj, Faculty of Electrical Engineering and Loiacono, Daniele, Politecnico di Milano Computing, Zagreb Lopes, Phil, École Polytechnique Fédéral de Lausanne Janikow, Cezary Z., UMSL López-Ibáñez, Manuel, University of Málaga Jaszkiewicz, Andrzej, Poznan University of Technology Louis, Sushil, UNR Jiménez Laredo, Juan Luis, Université Le Havre Normandie Lourenço, Nuno, University of Coimbra Jin, Yaochu, University of Surrey Ludwig, Simone A., North Dakota State University Johnson, Colin Graeme, The University of Nottingham Luengo, Julián, University of Granada José-García, Adán, University of Lille Luga, Hervé, Université de Toulouse Julstrom, Bryant Arthur, St. Cloud State University Luna, J. M., University of Cordoba, Spain Kaliakatsos-Papakostas, Maximos, Athena Research and In- Luo, Wenjian, Harbin Institute Of Technology, Shenzhen novation Centre Luo, Xiao, Indiana University-Purdue University Indianapolis Karakostas, George, McMaster University Luong, Ngoc Hoang, University of Information Technology Karns, Joshua, Rochester Institute of Technology (UIT), VNU-HCM Kattan, Ahmed, UQU Luque, Gabriel, University of Malaga Kaufmann, Paul, University of Mainz MA, LEI, Kyushu University Kayacik, Gunes, Okta MISIR, Mustafa, Istinye University Keedwell, Ed, University of Exeter Ma, Hui, Victoria University of Wellington Kerschke, Pascal, University of Münster Machado, Penousal, University of Coimbra Kheiri, Ahmed, Lancaster University Malan, Katherine M., University of South Africa Knowles, Joshua, Invenia Labs Manderick, Bernard, VUB Kolodziej, Joanna, Cracow University of Technology Manzoni, Luca, Università degli Studi di Trieste Kordon, Arthur, Kordon Consulting LLC Maree, Stefanus, Amsterdam UMC Korošec, Peter, Jožef Stefan Institute Mariano, Pedro, BioISI - Faculdade de Ciências da Universi- Kramer, Oliver, University of Oldenburg dade de Lisboa Krejca, Martin S., Hasso Plattner Institute Martínez, Ivette C., Universidad Simón Bolívar Kronberger, Gabriel, University of Applied Sciences Upper Mavrovouniotis, Michalis, University of Cyprus Austria Mayer, Helmut A., University of Salzburg Kuber, Karthik, Loblaw Companies Limited McCall, John, Smart Data Technologies Centre Kötzing, Timo, Hasso Plattner Institute McCormack, Jon, Monash University Lahoz-Beltra, Rafael, Complutense University of Madrid McDermott, James, National University of Ireland, Galway LaCava, William, University of Pennsylvania McIntyre, Andrew Ryan, Dalhousie University 10 Program Committee

Medvet, Eric, DIA, University of Trieste, Italy partment of Electronics and Informatics (ETRO) and imec Mehnen, Jorn, University of Strathclyde Parkes, Andrew J., University of Nottingham Mei, Yi, Victoria University of Wellington Parsopoulos, Konstantinos, University of Ioannina Melab, Nouredine, Université Lille 1, CNRS/CRIStAL, Inria Pedemonte, Martín, Instituto de Computación, Facultad de Lille Ingeniería, Universidad de la República Mendiburu, Alexander, University of the Basque Country Pei, Wenbin, Victoria University of Wellington UPV/EHU Pellegrini, Paola, Université Gustave EIFFEL Menzel, Stefan, Honda Research Institute Europe Pelta, David, University of Granada Merelo, JJ, University of Granada Peng, Xingguang, Northwestern Polytechnical University Meyer-Nieberg, Silja, Universitaet der Bundeswehr Pereira, Francisco Baptista, Instituto Superior de Engenharia Muenchen de Coimbra, Portugal Meyerson, Elliot, Cognizant Technology Solutions Pereira, Jordi, Universidad Adolfo Ibáñez Mezura-Montes, Efren, University of Veracruz Perez-Liebana, Diego, Queen Mary University of London Michalak, Krzysztof, Wroclaw University of Economics Picek, Stjepan, Delft University of Technology Miettinen, Kaisa, University of Jyvaskyla Pilat, Martin, Charles University, Faculty of Mathematics and Miller, Julian F., University of York Physics Minetti, Gabriela, Universidad Nacional de La Pampa, Facul- Pitra, Zbynˇek, Faculty of Nuclear Sciences and Physical Engi- tad de Ingeniería neering of the Czech Technical University Miramontes Hercog, Luis, Eclectic Systems Pizzuti, Clara, Institute for High Performance Computing and Miranda Valladares, Gara, Universidad de La Laguna Networking - ICAR National Research Council of Italy - Monmarché, Nicolas, University of Tours CNR Montero, Elizabeth, Universidad Andres Bello Pluhacek, Michal, Tomas Bata University in Zlin Moore, Jason, University of Pennsylvania Pop, Petrica, Technical University of Cluj-Napoca, North Uni- Moritz, Steffen, Technische Hochschule Köln versity Center at Baia Mare, Romania Mouret, Jean-Baptiste, Inria / CNRS / Univ. Lorraine Porumbel, Daniel, CEDRIC, CNAM (Conservatoire National Mumford, Christine Lesley, Cardiff University des Arts et Métiers) Musliu, Nysret, Vienna University of Technology Potena, Pasqualina, RISE Research Institutes of Sweden AB Nakata, Masaya, Yokohama National University Prandtstetter, Matthias, AIT Austrian Institute of Technology Nalepa, Jakub, Silesian University of Technology GmbH Naujoks, Boris, TH Köln - University of Applied Sciences Prestwich, Steve, University College Cork Nebro, Antonio J., University of Málaga Preuss, Mike, Universiteit Leiden Neumann, Aneta, The University of Adelaide Puchinger, Jakob, IRT SystemX Nguyen, Duc Manh, Hanoi National University of Education Pérez Cáceres, Leslie, Pontificia Universidad Católica de Val- Nguyen, Quang Uy, University College Dublin paraíso Nguyen, Su, La Trobe University Qian, Chao, Nanjing University Nicolau, Miguel, University College Dublin Qiu, Xin, National University of Singapore Nieto, Ricardo, Centro de Investigación en Matemáticas A.C Quagliarella, Domenico, CIRA — Italian Center for Aerospace Nikfarjam, Adel, The University of Adelaide Research Nitschke, Geoff, University of Cape Town Raidl, Günther R., TU Wien Nojima, Yusuke, Osaka Prefecture University Rajabi, Amirhossein, Technical University of Denmark O’Neill, Michael, University College Dublin Ramirez, Cristian, Universidad Politécnica de Madrid O’Reilly, Una-May, CSAIL, Massachusetts Institute of Tech- Rebolledo Coy, Margarita, TH Köln nology Reed, Patrick M., Cornell University Olague, Gustavo, CICESE Rehbach, Frederik, TH Köln Olhofer, Markus, Honda Research Institute Europe GmbH Reis, Gustavo, School of Technology and Management, Poly- Oliva, Diego, Universidad de Guadalajara technic of Leiria, Portugal Oliveto, Pietro S., The University of Sheffield Rhyd, Lewis, PC Member Omidvar, Mohammad Nabi, University of Leeds Riff, Maria Cristina, UTFSM Otero, Fernando, University of Kent Rigoni, Enrico, ESTECO SpA Oyama, Akira, Institute of Space and Astronautical Science, Risi, Sebastian, IT University of Copenhagen Japan Aerospace Exploration Agency Ritt, Marcus, Federal University of Rio Grande do Sul Özcan, Ender, University of Nottingham Rockett, Peter, University of Sheffield Panichella, Annibale, Delft University of Technology Rodriguez-Tello, Eduardo, CINVESTAV - Tamaulipas Papa, Gregor, Jožef Stefan Institute Rodríguez, David, UNET Papavasileiou, Evgenia, Vrije Universiteit Brussel (VUB), De- Rojas-Morales, Nicolas, Universidad Técnica Federico Santa Program Committee 11

María Soros, Lisa, University of Central Florida Romero, José Raúl, University of Córdoba Sosa Hernandez, Victor Adrian, ITESM-CEM Rosales-Pérez, Alejandro, Centro de Investigación en Spector, Lee, Hampshire College Matemáticas Spettel, Patrick, Vorarlberg University of Applied Sciences Ross, Brian J., Brock University Stein, Anthony, University of Hohenheim Rothlauf, Franz, University of Mainz Stich, Sebastian, École Polytechnique Fédérale de Lausanne Rowe, Jonathan, University of Birmingham Stoean, Catalin, University of Craiova, Romania Rudolph, Guenter, TU Dortmund University Stolfi, Daniel H., University of Luxembourg Ruiz, Ruben, Polytechnic University of Valencia Stork, Jörg, Institute of Data Science, Engineering, and Ana- Runarsson, Thomas, University of Iceland lytics, TH Köln Runkler, Thomas, Siemens AG Stützle, Thomas, Université Libre de Bruxelles Ryan, Conor, University of Limerick Sudholt, Dirk, University of Passau Saborido, Rubén, University of Malaga Sun, Chaoli, Taiyuan University of Science and Technology Sahraoui, Houari, DIRO, Université de Montréal Sun, Yanan, Sichuan University Sakamoto, Naoki, University of Tsukuba Suzuki, Reiji, Nagoya University Salto, Carolina, Fac. de Ingeniería - UNLPam - Argentina Takadama, Keiki, The University of Electro-Communications Samothrakis, Spyridon, University of Essex Takahashi, Ricardo, Universidade Federal de Minas Gerais Sanches, Danilo Sipoli, Federal University of Technology of Tanabe, Ryoji, Yokohama National University Parana Tanev, Ivan, Faculty of Engineering, Doshisha University Sanchez, Luciano, Universidad de Oviedo Tang, Yujin, Google Sanchis Sáez, Javier, Universitat Politècnica de València Tarantino, Ernesto, ICAR - CNR Santini, Alberto, Universitat Pompeu Fabra Tarapore, Danesh, University of Southampton Santucci, Valentino, University for Foreigners of Perugia Tari, Sara, Université du Littoral Côte d’Opale Sato, Yuji, Hosei University Tauritz, Daniel R., Auburn University Saubion, Fredéric, University of Angers, France Tavares, Roberto, Federal University of Sao Carlos Saurabh, Saket, IMSc Terashima-Marín, Hugo, Tecnológico de Monterrey Schaefer, Robert, AGH University of Science and Technology Terrazas Angulo, German, University of Cambridge Schmitt, Sebastian, Honda Research Institute Europe GmbH Tettamanzi, Andrea G. B., Université Côte d’Azur Schrum, Jacob, Department of Mathematics and Computer Textor, Johannes, University of Utrecht Science, Southwestern University Thawonmas, Ruck, Ritsumeikan University Scirea, Marco, University of Southern Denmark Thierens, Dirk, Utrecht University Sebag, Michele, Université Paris-Sud Thiruvady, Dhananjay, Deakin University Segovia-Dominguez, Ignacio, Center for Research in Mathe- Thomas, Spencer, National Physical Laboratory matics Thomson, Sarah, University of Stirling Segredo, Eduardo, Universidad de La Laguna Tian, Jie, Taiyuan University of Science and Technology Sekanina, Lukas, Brno University of Technology, Czech Re- Tinós, Renato, University of São Paulo public Tirumala, Sreenivas Sremath, Manukau Institute of Technol- Sendhoff, Bernhard, Honda Research Institute Europe ogy Shi, Feng, Central South University Togelius, Julian, New York University Shirakawa, Shinichi, Yokohama National University Tonda, Alberto, UMR 518 MIA, INRAE, Paris Shukla, Praduymn, University of Manchester Torresen, Jim, University of Oslo Siddique, Abubakar, Victoria University of Wellington Toscano, Gregorio, Cinvestav-IPN Silva, Sara, University of Lisbon Toure, Cheikh, École Polytechnique Sim, Kevin, Edinburgh Napier University Toutouh, Jamal, Massachusetts Inst. of Technology Simonin, Olivier, INSA Lyon Tran, Binh, Victoria University of Wellington Simões, Anabela, DEIS/ISEC - Coimbra Polytechnic Trautmann, Heike, University of Münster Sinapayen, Lana, Sony Computer Science Laboratories, Inc. Trojanowski, Krzysztof, Cardinal Stefan Wyszy´nski University Sindhya, Karthik, FINNOPT Oy in Warsaw Singh, Hemant K., University of New South Wales Tumpach, Jiˇrí, The Czech Academy of Sciences Sipper, Moshe, Dept. of Computer Science Tusar, Tea, Jožef Stefan Institute Smith, Christopher, McLaren Automotive Ltd Urbano, Paulo, University of Lisbon Smith, Jim, University of the West of England Urbanowicz, Ryan, University of Pennsylvania Sobania, Dominik, University of Mainz Uribe, Lourdes, Instituto Politécnico Nacional Soltoggio, Andrea, Loughborough University Urquhart, Neil, Edinburgh Napier University Soria, Jorge, University of Guanajuato van Stein, Bas, LIACS, Leiden University 12 Program Committee

van der Blom, Koen, Leiden University tria Vanneschi, Leonardo, ISEGI, Universidade Nova de Lisboa Witt, Carsten, Technical University Of Denmark Vasconcellos Vargas, Danilo, Kyushu University Wong, Man Leung, Lingnan University, Hong Kong Vasicek, Zdenek, Brno University of Technology Xie, Huayang, Oracle New Zealand Vatolkin, Igor, TU Dortmund Xie, Yue, The University of Adelaide Veenstra, Frank, IT University of Copenhagen Xin-She, Yang, Middlesex University Veerapen, Nadarajen, University of Lille Xiong, Ning, Malardalen University Velasco, Jonas, CIMAT, A. C. Yamada, Takeshi, NTT Communication Science Labs. Verel, Sebastien, Université du Littoral Côte d’Opale Yang, Cuie, Hong Kong Baptist Unvirsity Vergilio, Silvia, Federal University of Paraná Yannakakis, Georgios N., Institute of Digital Games, Univer- Viana, Ana, INESC TEC/Polytechnic of Porto sity of Malta Vidnerova, Petra, Institute of Computer Science of ASCR Yevseyeva, Iryna, De Montfort University Vogel, Thomas, Humboldt University Berlin Yoo, Shin, Korea Advanced Institute of Science and Technol- Von Zuben, Fernando J., Unicamp ogy Vukasinovic, Vida, JSI Yu, Tian-Li, Department of Electrical Engineering, National Wagner, Markus, School of Computer Science, The University Taiwan University of Adelaide Zaefferer, Martin, TH Köln Wang, Bin, Victoria University of Wellington Zafra, Amelia, University of Cordoba Wang, Feng, Wuhan University Zapotecas-Martínez, Saúl, Universdad Autónoma Metropoli- Wang, Handing, Xidian University tana Wang, Hao, Sorbonne Université Zarges, Christine, Aberystwyth University Wang, Rui, Uber AI Labs Zhan, Zhi-Hui, South China University of Technology Wang, Shuai, Xidian University Zhang, Fangfang, Victoria University of Wellington Wang, Yifei, Georgia Tech Zhang, Xingyi, Anhui Unversity Wanka, Rolf, University of Erlangen-Nuremberg Zheng, Weijie, Southern University of Science and Technol- Wanner, Elizabeth, CEFET-MG ogy Wessing, Simon, Technische Universität Dortmund Zhong, Jinghui, South China University of Technology Whigham, Peter Alexander, Univ. of Otago Zhou, Aimin, Department of Computer Science and Technol- White, David, University of Sheffield ogy Whitley, Darrell, Colorado State University Zhou, Yan, Northeastern University Wilkerson, Josh, NAVAIR Zille, Heiner, Otto-von-Guericke University Magdeburg Wilson, Garnett, Dalhousie University Zincir-Heywood, Nur, Dalhousie University Winkler, Stephan, University of Applied Sciences Upper Aus- Program Committee (Companion) 13

Program Committee (Companion)

Adair, Jason, University of Stirling Bentley, Peter J., University College London Adekoya, Adekunle Rotimi, Stellenbosch University Bernardino, Heder, Universidade Federal de Juiz de Fora Affenzeller, Michael, University of Applied Science Upper (UFJF) Austria Beyer, Hans-Georg, Vorarlberg University of Applied Sciences Aguirre, Hernan, Shinshu University Bezerra, Leonardo, IMD, Universidade Federal do Rio Grande Akimoto, Youhei, University of Tsukuba do Norte Al-Helali, Baligh, Victoria University of Wellington Bi, Ying, Victoria University of Wellington al-Rifaie, Mohammad Majid, University of Greenwich Biedrzycki, Rafał, Institute of Computer Science, Warsaw Al-Sahaf, Harith, Victoria University of Wellington University of Technology Alaya, Ines, Ecole Nationale des Sciences de l’Informatique Bingham, Garrett, The University of Texas at Austin (ENSI) Biondi, Giulio, University of Perugia Albukhanajer, Wissam, Roke Manor Research Limited Birattari, Mauro, Université Libre de Bruxelles Alderliesten, Tanja, Leiden University Medical Center Blank, Julian, Michigan State University Alexander, Bradley James, University of Adelaide Blesa, Maria J., Universitat Politècnica de Catalunya - Ali, Shaukat, Simula Research Laboratory BarcelonaTech Allard, Maxime, Imperial College Blot, Aymeric, University College London Alvarez Gil, Nicolas, ArcelorMittal Booker, Lashon, The MITRE Corporation Amaya, Jhon Edgar, UNET Bosman, Anna, University of Pretoria Antipov, Denis, ITMO University Bosman, Peter A.N., Centre for Mathematics and Computer Antunes, Carlos, DEEC-UC Science Aranha, Claus, Graduate School of Systems Information Engi- Bossek, Jakob, University of Münster neering, University of Tsukuba Boumaza, Amine, Université de Lorraine / LORIA Arcaini, Paolo, National Institute of Informatics Bouter, Anton, CWI Arias Montaño, Alfredo, IPN-ESIME Bredeche, Nicolas, Sorbonne Université Arita, Takaya, Nagoya University Brockhoff, Dimo, INRIA Saclay - Ile-de-France Arnaldo, Ignacio, PatternEx Browne, Will, Victoria University of Wellington Arnold, Dirk, Dalhousie University Browne, Will Neil, Victoria University of Wellington Arza, Etor, Basque Center for Applied Mathematics Brownlee, Alexander, University of Stirling Assimi, Hirad, University of Adelaide Bruce, Bobby R., UC Davis Auger, Anne, INRIA Bucur, Doina, University of Twente, The Netherlands Aydın, Dogan, Dumlupınar University Bull, Larry, University of the West of England Aydin, Mehmet Emin, University of the West of England Burlacu, Bogdan, University of Applied Sciences Upper Aus- Azad, Raja Muhammad Atif, Birmingham City University tria Bacardit, Jaume, Newcastle University Butz, Martin V., University of Tübingen Bagheri, Samineh, TH Köln - University of Applied Sciences Buzdalov, Maxim, ITMO University Baioletti, Marco, University of Perugia Buzdalova, Arina, ITMO University Banzhaf, Wolfgang, Michigan State University Byrski, Aleksander, AGH University of Science and Technol- Barbosa, Helio J.C., LNCC ogy Barros, Gabriella, New York University Bäck, Thomas, Leiden University Barros, Marcio, UNIRIO Cagnoni, Stefano, University of Parma Barros, Rodrigo, FACIN-PUCRS Camacho, David, Universidad Politécnica de Madrid Bassett, Jeffrey K., Group W, Inc. Camacho Villalón, Christian Leonardo, Université Libre de Basseur, Matthieu, Université d’Angers Bruxelles Basterrech, Sebastian, VSB-Technical University of Ostrava Camero Unzueta, Andrés, University of Malaga Basto-Fernandes, Vitor, University Institute of Lisbon Candadai, Madhavun, Indiana University Batista, Lucas, Universidade Federal de Minas Gerais Capodieci, Nicola, university of modena and reggio emilia Bazargani, Mosab, Queen Mary University of London Carballedo, Roberto, University of Deusto Bechikh, Slim, University of Tunis Carmelo J A, Bastos Filho, University of Pernambuco, Po- Beham, Andreas, University of Applied Sciences Upper Aus- litechnic School of Pernambuco tria Castelli, Mauro, NOVA IMS, Universidade Nova de Lisboa Belavkin, Roman, Middlesex University London Castillo, Pedro, UGR Belkhir, Nacim, Safran Cauwet, Marie-Liesse, ESIEE Paris Ben Romdhane, Hajer, Institut Supérieur de Gestion de Tunis Ceberio, Josu, University of the Basque Country 14 Program Committee (Companion)

Ceschia, Sara, University of Udine Desell, Travis, Rochester Institute of Technology Chacón Castillo, Joel, CIMAT Deutz, Andre, Leiden University Chakraborty, Uday, University of Missouri Dick, Grant, University of Otago Chelly Dagdia, Zaineb, Versailles Saint-Quentin-en-Yvelines Diosan, Laura, Babes Bolyai University, Computer Science University, Paris Saclay Department Chen, Boyuan, Columbia University Divina, Federico, Pablo de Olavide University Chen, Gang, Victoria University of Wellington Do, Anh, The University of Adelaide Chen, Qi, Victoria University of Wellington Dockhorn, Alexander, Queen Mary University of London Chen, Wei-Neng, South China University of Technology Doerner, Karl F., University of Vienna Chen, Ying-ping, National Chiao Tung University Doerr, Benjamin, Ecole Polytechnique Cheng, Ran, Southern University of Science and Technology Doerr, Carola, CNRS Chenu, Alexandre, Sorbonne Université, ISIR Dolson, Emily L., Michigan State University Chicano, Francisco, University of Malaga Doncieux, Stéphane, Sorbonne Universite Chlebik, Miroslav, University of Sussex Dorin, Alan, Monash University Chotard, Alexandre Adrien, KTH Dorronsoro, Bernabe, University of Cadiz Christensen, Anders Lyhne, University of Southern Denmark Dovgan, Erik, Jožef Stefan Institute Chugh, Tinkle, University of Exeter Drake, John, University of Leicester Chávez, Francisco, University of Extremadura Dreo, Johann, Thales Research & Technology Cleghorn, Christopher, University of the Witwatersrand Drezewski, Rafal, AGH University of Science and Technology Clemente, Eddie, CICESE Drugan, Madalina, ITLearns.Online Coello Coello, Carlos A., CINVESTAV-IPN Duarte, Abraham, Universidad Rey Juan Carlos Colanzi, Thelma Elita, Universidade Estadual de Maringá Durillo, Juan, Leibniz Supercomputing Center of the Bavarian Colomine, Feijoo, UNET Academy of Science and Humanities Coninx, Alex, Sorbonne Universite Duro, Joao, The University of Sheffield Correia, João, Center for Informatics and Systems of the Uni- Duro, Richard, Universidade da Coruña versity of Coimbra Dwianto, Yohanes Bimo, The University of Tokyo Cortez, Paulo, University of Minho D’Andreagiovanni, Fabio, CNRS, UTC - Sorbonne University, Costa, Ernesto, University of Coimbra France Costa, Victor, University of Coimbra Díaz, Diego, ArcelorMittal Craven, Matthew, Plymouth University Dórn, Marcio, Federal University of Rio Grande do Sul Cuate, Oliver, ESFM-IPN Ebner, Marc, Ernst-Moritz-Universität Greifswald, Germany Cui, Xiaodong, IBM T. J. Watson Research Center Eftimov, Tome, Jožef Stefan Institute Cully, Antoine, Imperial College Ekart, Aniko, Aston University Cussat-Blanc, Sylvain, University of Toulouse El-Abd, Mohammed, American University of Kuwait Delavernhe, Florian, Université d’Angers ElSaid, AbdElRahman, Rochester Institute of Technology Dang, Duc-Cuong, University of Southampton Engelbrecht, Andries P., Stellenbosch University Dang, Nguyen, University of St Andrews Eremeev, Anton V., Omsk Branch of Sobolev Institute of Math- Danoy, Gregoire, University of Luxembourg ematics Datta, Dilip, Tezpur University Ernst, Andreas, Monash University De Ath, George, University of Exeter Escalante, Hugo, Instituto Nacional de Astrofisica, Optica y De Jong, Kenneth, George Mason University Electronica, Mexico de la Fraga, Luis Gerardo, CINVESTAV Everson, Richard, University of Exeter De Lorenzo, Andrea, DIA - University of Trieste Feng, Liang, Chongqing University de Nobel, Jacob, Leiden University Ferariu, Lavinia, "Gheorghe Asachi" Technical University of de Oliveira Neto, Francisco Gomes, Chalmers and the Univer- Iasi sity of Gothenburg Fernandes, Carlos, ISR de Sa, Alex, University of Melbourne Ferrante, Eliseo, VU University Amsterdam de Souza, Marcelo, UDESC Ferrer, Javier, University of Malaga Deb, Kalyanmoy, Michigan State University Festa, Paola, Universitá degli Studi di Napoli Deist, Timo, Centrum Wiskunde & Informatica (CWI) Fieldsend, Jonathan Edward, University of Exeter Delgado, Myriam, Federal University of Technology – Paraná Finck, Steffen, FH Vorarlberg University of Applied Sciences Delgado-Pérez, Pedro, Universidad de Cádiz Fischbach, Andreas, TH Köln Della Cioppa, Antonio, Natural Computation Lab - DIEM, Fister Jr., Iztok, University of Maribor University of Salerno Flageat, Manon, Imperial College Derbel, Bilel, Univ. Lille Fleck, Philipp, University of Applied Sciences Upper Austria Program Committee (Companion) 15

Folino, Gianluigi, ICAR-CNR Hernando, Leticia, University of the Basque Country Fontaine, Matthew, University of Southern California Hernández-Aguirre, Arturo, Centre for Research in Mathe- Franzin, Alberto, Université Libre de Bruxelles matics Freitas, Alex A., University of Kent Heywood, Malcolm, Dalhousie University Gagné, Christian, Université Laval Hintze, Arend, Dalarna University Galeotti, Juan Pablo, University of Buenos Aires Holdener, Ekaterina, Saint Louis University Gallagher, Marcus R., University of Queensland Holena, Martin, Institute of Computer Science Galvan, Edgar, Maynooth University Holzinger, Florian, University of Applied Sciences Upper Gao, Xiaoying, Victoria University of Wellington Austria Garcia-Nieto, Jose, University of Malaga Hoover, Amy K., New Jersey Institute of Technology García-Martínez, Carlos, Univ. of Córdoba Howard, Gerard, CSIRO Gay, Gregory, University of South Carolina Hu, Ting, Queen’s University Gelareh, Shahin, University of Artois/ LGI2A Hu, Weiming, PSU Gentile, Lorenzo, TH Köln - University of Applied Sciences Huizinga, Joost, Uber Technologies Inc. Giacobini, Mario, University of Torino Husbands, Phil, Sussex University Giavitto, Jean-Louis, CNRS - IRCAM, Sorbonne Université Hähner, Jörg, University of Augsburg Giustolisi, Orazio, Technical University of Bari Iacca, Giovanni, University of Trento Glasmachers, Tobias, Ruhr-University Bochum Ikegami, Takashi, University of Tokyo Glette, Kyrre, University of Oslo Inden, Benjamin, Nottingham Trent University Goeffon, Adrien, LERIA, University of Angers Iori, Manuel, University of Modena and Reggio Emilia Gonzalez Coto, Marcos, ArcelorMittal Irurozki, Ekhine, Telecom Paris González de Prado Salas, Pablo, Foqum Ishibuchi, Hisao, Osaka Prefecture University Gonçalves, Ivo, INESC Coimbra, DEEC, University of Coim- Izquierdo, Eduardo, Indiana University bra Jakobovic, Domagoj, Faculty of Electrical Engineering and Goodman, Erik, Michigan State University Computing, Zagreb Graff, Mario, Universidad Michoacana de San Nicolas de Jalali, Seyed Mohammad Jafar, Deakin University yahui Janikow, Cezary Z., UMSL Gravina, Daniele, Institute of Digital Games, University of Jaszkiewicz, Andrzej, Poznan University of Technology Malta Jiménez Laredo, Juan Luis, Université Le Havre Normandie Greiner, David, Universidad de Las Palmas de Gran Canaria Jin, Yaochu, University of Surrey Grillotti, Luca, Imperial College Johnson, Colin Graeme, The University of Nottingham Grimme, Christian, Münster University José-García, Adán, University of Lille Groleaz, Lucas, LIRIS Julstrom, Bryant Arthur, St. Cloud State University Guerreiro, Andreia P., INESC-ID Kadavy, Tomas, Tomas Bata University in Zlin Guizzo, Giovani, University College London Kaliakatsos-Papakostas, Maximos, Athena Research and In- Ha, David, Google Brain novation Centre Haddow, Pauline Catriona, NTNU Kalkreuth, Roman, TU Dortmund Hakanen, Jussi, University of Jyvaskyla Karakostas, George, McMaster University Hamann, Heiko, Institute of Computer Engineering, Univer- Karder, Johannes, University of Applied Sciences Upper Aus- sity of Lübeck tria Hancer, Emrah, Mehmet Akif Ersoy Üniversitesity Karns, Joshua, Rochester Institute of Technology Handa, Hisashi, Kindai University Kattan, Ahmed, UQU Handl, Julia, Mrs Kaufmann, Paul, University of Mainz Hao, Jin-Kao, University of Angers - LERIA Kayacik, Gunes, Okta Harrison, Kyle Robert, The University of New South Wales Keedwell, Ed, University of Exeter Hart, Emma, Edinburgh Napier University Kerschke, Pascal, University of Münster He, Cheng, Southern University of Science and Technology Kheiri, Ahmed, Lancaster University He, Jun, University of Wales, Aberystwyth Knowles, Joshua, Invenia Labs Helbig, Mardé, Griffith University Kolodziej, Joanna, Cracow University of Technology Hellwig, Michael, Vorarlberg University of Applied Sciences Kommenda, Michael, University of Applied Sciences Upper Helmuth, Thomas, Hamilton College Austria Hemberg, Erik, MIT CSAIL Kononova, Anna, Leiden University Hendtlass, Tim, Swinburne University of Technology Kordon, Arthur, Kordon Consulting LLC Hernandez-Riveros, Jesús-Antonio, Universidad Nacional de Korošec, Peter, Jožef Stefan Institute Colombia Kotenko, Igor, St. Petersburg Institute for Informatics and 16 Program Committee (Companion)

Automation of the Russian Academy of Sciences (SPIIRAS) Luo, Jieliang, Autodesk Research Kramer, Oliver, University of Oldenburg Luo, Wenjian, Harbin Institute Of Technology, Shenzhen Krejca, Martin, Sorbonne University Luo, Xiao, Indiana University-Purdue University Indianapolis Krejca, Martin S., Hasso Plattner Institute Luong, Ngoc Hoang, University of Information Technology Kronberger, Gabriel, University of Applied Sciences Upper (UIT), VNU-HCM Austria Luque, Gabriel, University of Malaga Krömer, Pavel, VSB-TU Ostrava MA, LEI, Kyushu University Kuber, Karthik, Loblaw Companies Limited MISIR, Mustafa, Istinye University Kuckling, Jonas, Université Libre de Bruxelles Ma, Hui, Victoria University of Wellington Kötzing, Timo, Hasso Plattner Institute Machado, Penousal, University of Coimbra Künzel, Steven, Universität der Bundeswehr München Macé, Valentin, InstaDeep Lahoz-Beltra, Rafael, Complutense University of Madrid Mahapatra, Rabi, Texas A&M University Lucas, Flavien, University of Antwerp Malan, Katherine M., University of South Africa LaCava, William, University of Pennsylvania Manderick, Bernard, VUB LaTorre, Antonio, Universidad Politécnica de Madrid Manzalini, Antonio, TIM Laflaquière, Alban, SoftBank Robotics EU Manzoni, Luca, Università degli Studi di Trieste Lara-Cabrera, Raul, Universidad Politecnica de Madrid Maree, Stefanus, Amsterdam UMC Lardeux, Frédéric, University of Angers, France Mariano, Pedro, BioISI - Faculdade de Ciências da Universi- Legrand, Pierrick, Université de Bordeaux dade de Lisboa Lehman, Joel, Uber AI Labs Martínez, Ivette C., Universidad Simón Bolívar Lehre, Per Kristian, University of Birmingham Mason, Karl, National University of Ireland Galway Leibnitz, Kenji, National Institute of Information and Com- Mavrovouniotis, Michalis, University of Cyprus munications Technology Mayer, Helmut A., University of Salzburg Lengler, Johannes, ETH Zürich McCall, John, Smart Data Technologies Centre Lensen, Andrew, Victoria University of Wellington McCormack, Jon, Monash University Li, Bin, University of Science and Technology of China McDermott, James, National University of Ireland, Galway Li, Miqing, University of Birmingham McIntyre, Andrew Ryan, Dalhousie University Li, Xiangtao, Northeast Normal University Medvet, Eric, DIA, University of Trieste, Italy Li, Xiaodong, RMIT University Mehnen, Jorn, University of Strathclyde Liapis, Antonios, University of Malta Mei, Yi, Victoria University of Wellington Liefooghe, Arnaud, Univ. Lille Melab, Nouredine, Université Lille 1, CNRS/CRIStAL, Inria Lim, Bryan, Imperial College London Lille Limmer, Steffen, Honda Research Institute Mendiburu, Alexander, University of the Basque Country Liu, Hai-Lin, Guangdong University of Technology UPV/EHU Liu, Jialin, Southern University of Science and Technology Menzel, Stefan, Honda Research Institute Europe Liu, Yiping, Hunan University Merelo, JJ, University of Granada Lo Bosco, Giosuè, Università degli studi di Palermo Meunier, Laurent, Paris Dauphine University Lobo, Daniel, University of Maryland, Baltimore County Meyer-Nieberg, Silja, Universitaet der Bundeswehr Lobo, Fernando G., University of Algarve Muenchen Lobo Pappa, Gisele, Federal University of Minas Gerais - Meyerson, Elliot, Cognizant Technology Solutions UFMG Mezura-Montes, Efren, University of Veracruz Loginov, Alexander, Dalhousie University Michalak, Krzysztof, Wroclaw University of Economics Loiacono, Daniele, Politecnico di Milano Miettinen, Kaisa, University of Jyvaskyla Lones, Michael, Heriot-Watt University Miikkulainen, Risto, The University of Texas at Austin Lopes, Phil, École Polytechnique Fédéral de Lausanne Milani Fard, Amin, NYiT López-Ibáñez, Manuel, University of Málaga Miller, Julian F., University of York Louis, Sushil, UNR Minetti, Gabriela, Universidad Nacional de La Pampa, Facul- Lourenço, Nuno, University of Coimbra tad de Ingeniería Lozano, Manuel, University of Granada (SPAIN) Miramontes Hercog, Luis, Eclectic Systems Lucas, Simon, Queen Mary University of London Miranda Valladares, Gara, Universidad de La Laguna Lucas, Simon, University of Essex Mitra, Kishalay, Indian Institute of Technology Hyderabad Ludwig, Simone A., North Dakota State University Monmarché, Nicolas, University of Tours Luengo, Julián, University of Granada Montero, Elizabeth, Universidad Andres Bello Luga, Hervé, Université de Toulouse Moore, Jason, University of Pennsylvania Luna, J. M., University of Cordoba, Spain Morel, Aurélien, Université Pierre et Marie Curie Program Committee (Companion) 17

Moritz, Steffen, Technische Hochschule Köln Pitra, Zbynˇek, Faculty of Nuclear Sciences and Physical Engi- Mouret, Jean-Baptiste, Inria / CNRS / Univ. Lorraine neering of the Czech Technical University Mumford, Christine Lesley, Cardiff University Pitzer, Erik, University of Applied Sciences Upper Austria Munoz Acosta, Mario Andres, The University of Melbourne Pizzuti, Clara, Institute for High Performance Computing and Musliu, Nysret, Vienna University of Technology Networking - ICAR National Research Council of Italy - Nakata, Masaya, Yokohama National University CNR Nalepa, Jakub, Silesian University of Technology Pluhacek, Michal, Tomas Bata University in Zlin Naujoks, Boris, TH Köln - University of Applied Sciences Pop, Petrica, Technical University of Cluj-Napoca, North Uni- Nebro, Antonio J., University of Málaga versity Center at Baia Mare, Romania Neumann, Aneta, The University of Adelaide Pope, Aaron Scott, Los Alamos National Laboratory Nguyen, Duc Manh, Hanoi National University of Education Porumbel, Daniel, CEDRIC, CNAM (Conservatoire National Nguyen, Quang Uy, University College Dublin des Arts et Métiers) Nguyen, Su, La Trobe University Potena, Pasqualina, RISE Research Institutes of Sweden AB Nicolau, Miguel, University College Dublin Prandtstetter, Matthias, AIT Austrian Institute of Technology Nieto, Ricardo, Centro de Investigación en Matemáticas A.C GmbH Nikfarjam, Adel, The University of Adelaide Prestwich, Steve, University College Cork Nitschke, Geoff, University of Cape Town Preuss, Mike, Universiteit Leiden Nojima, Yusuke, Osaka Prefecture University Puchinger, Jakob, IRT SystemX O’Neill, Michael, University College Dublin Purshouse, Robin, University of Sheffield O’Reilly, Una-May, CSAIL, Massachusetts Institute of Tech- Pätzel, David, University of Augsburg nology Pérez, Aritz, Basque Center for Applied Mathematics Olague, Gustavo, CICESE Pérez Cáceres, Leslie, Pontificia Universidad Católica de Val- Olhofer, Markus, Honda Research Institute Europe GmbH paraíso Oliva, Diego, Universidad de Guadalajara Qian, Chao, Nanjing University Oliveto, Pietro S., The University of Sheffield Qiao, Yukai, University of Queensland Omidvar, Mohammad Nabi, University of Leeds Qiu, Xin, National University of Singapore Oplatkova, Zuzana, Tomas Bata University in Zlin Quagliarella, Domenico, CIRA — Italian Center for Aerospace Orzechowski, Patryk, University of Pennsylvania Research Otero, Fernando, University of Kent Ruiz, Ana Belen, Universidad De Malaga Oyama, Akira, Institute of Space and Astronautical Science, Raggl, Sebastian, University of Applied Sciences Upper Aus- Japan Aerospace Exploration Agency tria Özcan, Ender, University of Nottingham Raidl, Günther R., TU Wien Pagnozzi, Federico, Université Libre de Bruxelles Rajabi, Amirhossein, Technical University of Denmark Palar, Pramudita Satria, Institut Teknologi Bandung Rakicevic, Nemanja, Imperial College Panichella, Annibale, Delft University of Technology Ramirez, Cristian, Universidad Politécnica de Madrid Papa, Gregor, Jožef Stefan Institute Rebolledo Coy, Margarita, TH Köln Papavasileiou, Evgenia, Vrije Universiteit Brussel (VUB), De- Reed, Patrick M., Cornell University partment of Electronics and Informatics (ETRO) and imec Regnier-Coudert, Olivier, Airbus Parkes, Andrew J., University of Nottingham Rehbach, Frederik, TH Köln Parsopoulos, Konstantinos, University of Ioannina Reis, Gustavo, School of Technology and Management, Poly- Pedemonte, Martín, Instituto de Computación, Facultad de technic of Leiria, Portugal Ingeniería, Universidad de la República Rhyd, Lewis, PC Member Pei, Wenbin, Victoria University of Wellington Riff, Maria Cristina, UTFSM Pekar, Libor, Tomas Bata University in Zlin Rigoni, Enrico, ESTECO SpA Pellegrini, Paola, Université Gustave EIFFEL Risi, Sebastian, IT University of Copenhagen Pelta, David, University of Granada Ritt, Marcus, Federal University of Rio Grande do Sul Peng, Xingguang, Northwestern Polytechnical University Rockett, Peter, University of Sheffield Pereira, Francisco Baptista, Instituto Superior de Engenharia Rodriguez-Tello, Eduardo, CINVESTAV - Tamaulipas de Coimbra, Portugal Rodríguez, David, UNET Pereira, Jordi, Universidad Adolfo Ibáñez Rojas Gonzalez, Sebastian, University of Gent Perez-Liebana, Diego, Queen Mary University of London Rojas-Morales, Nicolas, Universidad Técnica Federico Santa Picek, Stjepan, Delft University of Technology María Pierrot, Thomas, InstaDeep Romero, José Raúl, University of Córdoba Pilat, Martin, Charles University, Faculty of Mathematics and Rosales-Pérez, Alejandro, Centro de Investigación en Physics Matemáticas 18 Program Committee (Companion)

Ross, Brian J., Brock University Sipper, Moshe, Dept. of Computer Science Rothlauf, Franz, University of Mainz Smith, Christopher, McLaren Automotive Ltd Rowe, Jonathan, University of Birmingham Smith, Jim, University of the West of England Rudolph, Guenter, TU Dortmund University Smith, Simón C., Imperial College Ruiz, Ruben, Polytechnic University of Valencia Sobania, Dominik, University of Mainz Runarsson, Thomas, University of Iceland Soltoggio, Andrea, Loughborough University Runkler, Thomas, Siemens AG Soria, Jorge, University of Guanajuato Ryan, Conor, University of Limerick Soros, Lisa, University of Central Florida Saborido, Rubén, University of Malaga Sosa Hernandez, Victor Adrian, ITESM-CEM Sachan, Swati, University of Manchester Spector, Lee, Hampshire College Sahraoui, Houari, DIRO, Université de Montréal Spettel, Patrick, Vorarlberg University of Applied Sciences Saini, Bhupinder Singh, University of Jyväskylä Stein, Anthony, University of Hohenheim Sakamoto, Naoki, University of Tsukuba Stibor, Thomas, GSI Helmholtz Centre for Heavy Ion Re- Salto, Carolina, Fac. de Ingeniería - UNLPam - Argentina search Samothrakis, Spyridon, University of Essex Stich, Sebastian, École polytechnique fédérale de Lausanne Sanches, Danilo Sipoli, Federal University of Technology of Stoean, Catalin, University of Craiova, Romania Parana Stolfi, Daniel H., University of Luxembourg Sanchez, Luciano, Universidad de Oviedo Stork, Jörg, Institute of Data Science, Engineering, and Ana- Sanchis Sáez, Javier, Universitat Politècnica de València lytics, TH Köln Santini, Alberto, Universitat Pompeu Fabra Stützle, Thomas, Université Libre de Bruxelles Santucci, Valentino, University for Foreigners of Perugia Sudholt, Dirk, University of Passau Sato, Yuji, Hosei University Sun, Chaoli, Taiyuan University of Science and Technology Saubion, Fredéric, University of Angers, France Sun, Yanan, Sichuan University Saurabh, Saket, IMSc Sural, Shamik, IIT Kharagpur Saxena, Dhish, Indian Institute of Technology Roorkee Sutton, Andrew M., University of Minnesota Duluth Schaefer, Robert, AGH University of Science and Technology Suzuki, Reiji, Nagoya University Schmitt, Sebastian, Honda Research Institute Europe GmbH Swan, Jerry, NNAISENSE SA Schrum, Jacob, Department of Mathematics and Computer Szczepanski, Nicolas, CRIStAL Science, Southwestern University Takadama, Keiki, The University of Electro-Communications Schulenburg, Sonia, Level E Research Limited Takahashi, Ricardo, Universidade Federal de Minas Gerais Schuman, Catherine, Oak Ridge National Laboratory Tanabe, Ryoji, Yokohama National University Schäpermeier, Lennart, University of Münster Tanev, Ivan, Faculty of Engineering, Doshisha University Scirea, Marco, University of Southern Denmark Tang, Yujin, Google Sebag, Michele, Université Paris-Sud Tarantino, Ernesto, ICAR - CNR Segovia-Dominguez, Ignacio, Center for Research in Mathe- Tarapore, Danesh, University of Southampton matics Tari, Sara, Université du Littoral Côte d’Opale Segredo, Eduardo, Universidad de La Laguna Tauritz, Daniel R., Auburn University Segura, Carlos, Centro de Investigación en Matemáticas Tavares, Roberto, Federal University of Sao Carlos (CIMAT) Terashima-Marín, Hugo, Tecnológico de Monterrey Sekanina, Lukas, Brno University of Technology, Czech Re- Terrazas Angulo, German, University of Cambridge public Tettamanzi, Andrea G. B., Université Côte d’Azur Sen, Sevil, Hacettepe University, Turkey Textor, Johannes, University of Utrecht Sendhoff, Bernhard, Honda Research Institute Europe Thawonmas, Ruck, Ritsumeikan University Shi, Feng, Central South University Thierens, Dirk, Utrecht University Shirakawa, Shinichi, Yokohama National University Thiruvady, Dhananjay, Deakin University Shukla, Praduymn, University of Manchester Thomas, Spencer, National Physical Laboratory Siddique, Abubakar, Victoria University of Wellington Thomson, Sarah, University of Stirling Sigaud, Olivier, Sorbonne Universite Tian, Jie, Taiyuan University of Science and Technology Silva, Sara, University of Lisbon Tinós, Renato, University of São Paulo Sim, Kevin, Edinburgh Napier University Tirumala, Sreenivas Sremath, Manukau Institute of Technol- Simonin, Olivier, INSA Lyon ogy Simões, Anabela, DEIS/ISEC - Coimbra Polytechnic Togelius, Julian, New York University Sinapayen, Lana, Sony Computer Science Laboratories, Inc. Tomassini, Marco, University of Lausanne Sindhya, Karthik, FINNOPT Oy Tomforde, Sven, Kiel University Singh, Hemant K., University of New South Wales Tonda, Alberto, UMR 518 MIA, INRAE, Paris Program Committee (Companion) 19

Topa, Pawel, AGH University of Science and Technology Wanka, Rolf, University of Erlangen-Nuremberg Torra, Vicenc, University of Skovde Wanner, Elizabeth, CEFET-MG Torresen, Jim, University of Oslo Was, Jaroslaw, AGH University of Science and Technology, Toscano, Gregorio, Cinvestav-IPN Poland Toure, Cheikh, École Polytechnique Werth, Bernhard, University of Applied Sciences Upper Aus- Toutouh, Jamal, Massachusetts Inst. of Technology tria Tran, Binh, Victoria University of Wellington Wessing, Simon, Technische Universität Dortmund Trautmann, Heike, University of Münster Whigham, Peter Alexander, Univ. of Otago Trojanowski, Krzysztof, Cardinal Stefan Wyszy´nski University White, David, University of Sheffield in Warsaw Whitley, Darrell, Colorado State University Trunfio, Giuseppe A., University of Sassari Wiegand, Paul, Winthrop University Tumpach, Jiˇrí, The Czech Academy of Sciences Wilkerson, Josh, NAVAIR Tureckova, Alzbeta, Tomas Bata University in Zlin Wilson, Garnett, Dalhousie University Tusar, Tea, Jožef Stefan Institute Winkler, Stephan, University Of Applied Sciences Upper Aus- Ungredda, Juan, University of Warwick tria Urbano, Paulo, University of Lisbon Witt, Carsten, Technical University Of Denmark Urbanowicz, Ryan, University of Pennsylvania Wong, Man Leung, Lingnan University, Hong Kong Uribe, Lourdes, Instituto Politécnico Nacional Xie, Huayang, Oracle New Zealand Urquhart, Neil, Edinburgh Napier University Xie, Yue, The University of Adelaide van den Berg, Daan, University of Amsterdam Xin-She, Yang, Middlesex University van der Blom, Koen, Leiden University Xiong, Ning, malardalen university van Stein, Bas, LIACS, Leiden University Yamada, Takeshi, NTT Communication Science Labs. Vanneschi, Leonardo, ISEGI, Universidade Nova de Lisboa Yang, Cuie, Hong Kong Baptist Unvirsity Vasconcellos Vargas, Danilo, Kyushu University Yang, Kaifeng, University of Applied Sciences Upper Austria Vasicek, Zdenek, Brno University of Technology Yannakakis, Georgios N., Institute of Digital Games, Univer- Vatolkin, Igor, TU Dortmund sity of Malta Veenstra, Frank, IT University of Copenhagen Yevseyeva, Iryna, De Montfort University Veerapen, Nadarajen, University of Lille Yoo, Shin, Korea Advanced Institute of Science and Technol- Velasco, Jonas, CIMAT, A. C. ogy Verel, Sebastien, Université du Littoral Côte d’Opale Yu, Tian-Li, Department of Electrical Engineering, National Vergilio, Silvia, Federal University of Paraná Taiwan University Vermetten, Diederick L, Leiden University Zaefferer, Martin, TH Köln Viana, Ana, INESC TEC/Polytechnic of Porto Zafra, Amelia, University of Cordoba Vidnerova, Petra, Institute of Computer Science of ASCR Zamuda, Ales, University of Maribor Viktorin, Adam, Tomas Bata University in Zlín Zapotecas-Martínez, Saúl, Universdad Autónoma Metropoli- Villani, Marco, University of Modena and Reggio Emilia, Italy tana Vogel, Thomas, Humboldt University Berlin Zarges, Christine, Aberystwyth University Von Zuben, Fernando J., Unicamp Zenisek, Jan, University of Applied Sciences Upper Austria Vukasinovic, Vida, JSI Zhan, Zhi-Hui, South China University of Technology Wagner, Markus, School of Computer Science, The University Zhang, Fangfang, Victoria University of Wellington of Adelaide Zhang, Xingyi, Anhui Unversity Wagner, Neal, Systems & Technology Research Zheng, Weijie, Southern University of Science and Technol- Walter, Mathew, University of Plymouth ogy Wang, Bin, Victoria University of Wellington Zhong, Jinghui, South China University of Technology Wang, Feng, Wuhan University Zhou, Aimin, Department of Computer Science and Technol- Wang, Handing, Xidian University ogy Wang, Hao, Sorbonne Université Zhou, Yan, Northeastern University Wang, Rui, Uber AI Labs Zille, Heiner, Otto-von-Guericke University Magdeburg Wang, Shuai, Xidian University Zincir-Heywood, Nur, Dalhousie University Wang, Yifei, Georgia Tech Zipkin, Joseph, Massachusetts Institute of Technology 20 Time Zone

Time Zone

The schedule of GECCO 2021 appears in Lille’s time zone (UTC/GMT+2 hours, Central European Summer Time). For your convenience, we provide here the equivalent time in a few selected major cities around the world:

For New York (USA), please subtract 6 hours. For Toronto (Canada), please subtract 6 hours. For Chicago/Houston (USA), please subtract 7 hours. For Los Angeles/San Francisco (USA), please subtract 9 hours. For Vancouver (Canada), please subtract 9 hours. For Paris (France)/Madrid (Spain)/Frankfurt (Germany), consider the exact same time. For London (UK)/Lisbon (Portugal), please subtract 1 hour. For Sao Paulo (Brazil), please subtract 5 hours. For New Delhi (India), please add 3:30 hours. For Sydney/Melbourne (Australia), please add 8 hours. For Shanghai/Beijing (China), please add 6 hours. For Taipei (Taiwan), please add 6 hours. For Tokyo (Japan), please add 7 hours. For Johannesburg/Cape Town (South Africa), consider the exact same time. For Wellington (New Zealand), please add 10 hours. Guide for Attendees 21

Guide for Attendees

General information

GECCO’21 will be hosted on two virtual platforms, Whova and Gather. All talks (sessions, keynotes, workshops and other events involving oral presentations) will be streamed in Whova. Gather will be used for coffee breaks, poster sessions and other interactive events. Both platforms run in-browser and will be cross-linked to facilitate moving between them. Each registered participant will receive email with credentials shortly before the conference. Gather, Whova, and other facilities and guidelines can be accessed directly from the Virtual Conference page of the GECCO website, which is meant to serve as a landing page fo all participants. While Whova provides a convenient static view of the event and structured access to schedule and content (days, sessions, tracks, talks, etc.), Gather is a game-like environment where each participant controls a mobile customiz- able avatar that can interact with other participants face-to-face via video chats and in other ways. Our Gather map will emulate a virtual conference center and its surroundings, featuring spacious foyer, lecture rooms, info desks, exhibitions, drawing tables for technical discussions, and other facilities, including renderings of the landmarks of the beautiful city of Lille, France, the host of the conference. To connect better with other participants and so improve the experience, we strongly encourage all attendees to spend most of their time between sessions in Gather, interacting with other participants, visiting exhibitions and exploring the environment. Sessions and other talks will be accessible from Gather too, i.e. interacting with a “session object” will open the corresponding page in Whova. The platform will be running 24/7 for the entire conference period, so it can be enjoyed also from the less conveniently located time zones. See our promotional video, Gather demos and instructions for more details. If you have no prior experience with Gather, consider familiarizing yourself with the platform prior to the event. To sum up, as a general rule, we ask the participants to spend most of their time during oral sessions in Whova, and most of the remaining time in Gather. Our video streaming platform behind Whova is Zoom. Neither Zoom, Gather, nor Whova require dedicated software on your device. Whova offers a mature mobile app that has been praised by many users. Gather offers also a standalone application, though it’s still in beta.

Please note: While most of the guidelines provided below are universal, there are slight differences (marked in the text) in how they apply to conference talks and workshop talks.

Guidelines for all attendees

• Always use headphones to avoid echos and parasitic feedback.

• Pick a quiet space for giving your presentation or asking questions, to avoid disturbing background noise.

• Chats are actually great for asking questions without interrupting the speaker, let’s make use of the chat provided in Whova.

• You can ask questions in the chat during the talk; however, the speakers are free to choose to either answer them on the spot, or wait until the end of the talk.

• Please keep the following in mind that, in online events, the attention span of the audience tends to be shorter than in onsite events — this was one of the motivations for shortening the talks in this edition of GECCO — we kindly ask you to make an extra effort to stimulate interactions.

• Coffee breaks will be longer than usual, allowing for a lot of one-to-one interaction in Gather.

• Adhere to the ACM’s Policy Against Harassment, as requested during registration. 22 Guide for Attendees

Guidelines for speakers

• All talks should be given live – pre-recorded videos are meant only as a backup, to be used sparingly in case of serious technical difficulties.

• Enter your Whova session at least 15 minutes before the scheduled presentation/poster session time to check the equipment and screen sharing.

• Enable your camera whenever possible, to connect better to your audience and improve listeners’ experience. Have your camera at eye level and look into the camera when talking. Talk in front of an uncluttered background or use built-in functionality to blur your natural background or apply a professional looking background.

• Avoid quick flipping of slides – viewers with slower connections may experience significant lags. If you plan to play a video clip, consider slowing it down, for the same reason.

• Once the session is over, presenters are strongly encouraged to join their session room in Gather for addi- tional questions and (informal) discussions about their talk.

• Be ready to reply to (offline) questions that may arise in Whova after your talk.

Guidelines for presenters of Posters, Late-breaking Abstracts (LBAs), and Competition Entries

• Please note that, in order to address different time zones, the poster session in Gather will take place twice, in the 16:00-18:00 CEST timeslot on Monday and in the 9:00-10:20 CEST timeslot on Tuesday. The poster session room will be available for all attendees throughout the entire conference, starting from the first poster session onwards.

• Be prepared that some participants will spend 1 minute looking at your poster, others 5 mins and some others 15+ minutes.

• Instead of trying to include every single detail of the work on the poster, think about the three groups above, and how you can create a poster from which everyone takes home something useful. Spend time on deciding what to exclude from the full paper rather than meticulously including every detail.

• Ensure that the poster clearly presents the key take-home message.

• Think of what is the best visual way of illustrating this.

• Use colours that are contrasting.

• If your paper contains very large tables or result graphs, consider just showing parts of these, or the key information instead of the full detail.

• You can include a QR code that links to your paper online, so any interested visitor can take a picture and go directly to the paper.

• Decide on a logical and simple structure for the poster, so that a viewer can figure out how to read it even if you are not there to explain.

• Remember that the poster is expected to attract attention and spark a discussion, and not to be a replica of the paper. Guide for Attendees 23

Guidelines for session chairs

• Arrive at your session early, at least 15 minutes before the scheduled start, to ensure speakers have checked in, and have everything they need for their presentation. If time permits, check if they can share their screens.

• A volunteer will be connected to the session and available to assist you.

• During the session, make sure that attendees are muted, and that only speakers can share their screen and use annotations.

• In the unlikely event that a speaker faces technical issues, play the pre-recorded video of his/her presentation. The speaker should attend the session in order to answer questions. In the unlikely event that a speaker is absent in the Q&A part of the talk, announce a break until the next presentation is due to start.

• Do not start early, as participants may be moving between sessions/presentations.

• Introduce each speaker.

• For conference presentations only: Speakers are allocated 20 minutes for a presentation: 16 minutes for set up and presentation, and 4 minutes for questions. Make sure the speakers adhere to the maximum time allotted.

• During the talk, the audience is allowed to ask questions in the chat; however, the speakers are free to choose to either answer them on the spot, or wait until the end of the talk. In the latter case, you may allow the speaker to answer the questions in the chat alone, or help him/her at that by reading the questions out loud. However, if there are other (oral) questions from the audience during discussion, we recommend to intertwine them with those from the chat, in order to engage the audience.

• Moderate the Q&A session and manage the Q&A chat. If there are no questions, ask one yourself (time permitting). Do not start the next presentation early (or late).

• Let the conference organizers know of any problems or if adjustments are needed. Prevent accidental unmuting, disruptive screen sharing, or offensive behavior from the audience. Report serious cases of any such behavior to the organizers.

• Ensure that the schedule is respected by the speakers and that the session ends timely. Follow the scheduled order of talks, as well as presentation times.

• Once the session is over, announce the time available before for the next slot, and invite the audience to convene in the session room in Gather for additional questions and informal discussion. 24 Virtual Venue

Virtual Venue

GECCO’21 will be completely online, and part of the conference will use Gather, a virtual game-like environment. Below are the maps of the virtual venues for July 10-11, and for July 12-14. This is just the entry point, we invite you to explore the virtual venue, meet with other researchers there, and discover the surprises we prepared for you.

Figure 1: Virtual venue for July 10-11 (top) and July 12-14 (bottom).

In Gather, each participant controls a mobile customizable avatar that can interact with other participants face-to- face via video chats and in other ways. Our Gather map will emulate a virtual conference center and its surroundings, featuring multiple facilities, including lecture rooms, drawing tables for technical discussions, info desk, exhibitions etc. Keynotes, talks and other events will be directly accessible from Gather; this is also where the poster sessions will take place. We encourage you to spend most of your time between sessions in Gather, asking questions to speakers, interacting with other attendees, visiting exhibitions and exploring the environment, part of which will Virtual Venue 25

recreate the beautiful city of Lille, France, the host of the conference. Gather will be running 24/7 for the entire conference period, so you can enjoy it also from the less conveniently located time zones. See the Youtube video and Gather demos and instructions for more details. If you have no prior experience with Gather, consider familiarizing yourself with the platform prior to the event. You will also be given access to Whova, a more traditional virtual conference platform which provides a more structured access to conference content and schedule (days, sessions, tracks, talks). Sessions can be directly entered from Whova; however, access to poster sessions will be limited (only pre-recorded videos will be accessible from Whova).

Schedule 28 Schedule

Schedule at a Glance (with hyperlinks, all times CEST=GMT+2/UTC+2)

Saturday, July 10 Sunday, July 11 Monday, July 12 Tuesday, July 13 Wednesday, July 14

Gather Gather

Poster Session (II) Tutorials and Tutorials and (Gather) Gather Gather Workshops Workshops 09:00-10:20 08:30-10:20 08:30-10:20

Break (Gather) Break (Gather) Paper Sessions, Invited Keynote Paper Sessions ECiP, and HOP Marc Mézard

10:20-11:40 10:20-11:40 10:20-11:40 Tutorials and Tutorials, Workshops, Workshops and Competitions Break (Gather) Break (Gather) Break (Gather) 11:00-12:50 11:00-12:50 Welcome 12:00

Invited Keynote Paper Sessions Paper Sessions Joshua Tenenbaum and HOP Break (Gather) Break (Gather) 12:20-13:40 12:20-13:40 12:20-13:40

Break and Job Market Break (Gather) Break (Gather) Tutorials and Tutorials, Workshops, 13:40-14:20 Workshops and Competitions Paper Sessions, SIGEVO Keynote 13:30-15:20 13:30-15:20 ECiP, HOP, Paper Sessions Melanie Mitchell and HUMIES 14:20-15:40 14:20-15:40 14:20-15:40 Break (Gather) Break (Gather) SIGEVO Meeting, Women@GECCO Awards, and Closing Tutorials and Tutorials and 15:45-18:00 Workshops Workshops Poster Session (I) 15:40-17:10 Gather 16:00-17:50 16:00-17:50 (Gather) 16:00-18:00

Gather Gather Gather

All-Night Poster Session Schedule 29

Workshop and Tutorial Sessions, Saturday, July 10 (all times CEST=GMT+2/UTC+2)

08:30-10:20 11:00-12:50 13:30-15:20 16:00-17:50

Model-Based Evolutionary Statistical Analyses for Benchmarking Algorithms Meta-heuristic Stochastic Multiobjective Optimizers Optimization Algorithms 2.0 utorial Room 1 T Thierens, Bosman Eftimov, Korošec Brockhoff, Tušar Evolutionary Computation and Evolutionary Submodular Constraint-Handling Evolutionary Multi- and Evolutionary Deep Learning for Optimisation Techniques used with Many-Objective Optimization: Image Analysis, Signal Processing Evolutionary Algorithms Methodologies, Applications and and Pattern Recognition Demonstration utorial Room 2 T Zhang, Cagnoni Neumann, Neumann, Qian Coello Coello Deb, Blank Learning Classifier Systems: Representations for Runtime Analysis of Genetic Programming A From Principles to Modern Evolutionary Algorithms Evolutionary Algorithms: Tutorial Introduction Systems Basic Introduction utorial Room 3 T Stein, Nakata Rothlauf Lehre, Oliveto O’Reilly, Hemberg Hyper-heuristics Recent Advances in Particle Benchmarking: state-of- Evolution of Neural Swarm Optimization the-art and beyond Networks Analysis and Understanding 2021 utorial Room 4 T Tauritz, Woodward Cleghorn, Engelbrecht Auger, Hansen Miikkulainen Replicability and A Gentle Introduction Theoretical Foundations of Reproducibility in to Theory (for Non- Evolutionary Computation Evolutionary Optimization Theoreticians) for Beginners and Veterans utorial Room 5 T Paquete, López-Ibáñez Doerr Whitley VIZGEC – Visualisation ECADA – 11th Workshop on Evolutionary Computation for Methods in Genetic and the Automated Design of Algorithms Evolutionary Computation orkshop Room 1

W p. 46 p. 47 BBOB – Black EVORL – Evolutionary Reinforcement Learning Workshop Box Optimization Benchmarking orkshop Room 2

W p. 46 p. 47 IAM – 6th Workshop on Industrial Applications of PDEIM – Parallel and EAHPC – Evolutionary Metaheuristics Distributed Evolutionary Algorithms and HPC Inspired Methods orkshop Room 3

W p. 44 p. 48 p. 50 ECPERM – Evolutionary Computation for Permutation STUDENT – Student Workshop Problems orkshop Room 4

W p. 44 p. 49 LAHS – Landscape-Aware Heuristic Search DTEO – 4th GECCO EVOSOFT – Evolutionary Workshop on Computation Software Decomposition Techniques Systems in Evolutionary

orkshop Room 5 Optimization

W p. 45 p. 49 p. 50

Tutorials Workshops 30 Schedule

Workshop and Tutorial Sessions, Sunday, July 11 (all times CEST=GMT+2/UTC+2)

08:30-10:20 11:00-12:50 13:30-15:20 16:00-17:50

Runtime Analysis of Recent Advances in Coevolutionary Sequential Experimentation Population-based Landscape Analysis for Computation for by Evolutionary Algorithms Evolutionary Algorithms Optimisation and Learning Adversarial Deep Learning utorial Room 1 T Lehre, Oliveto Malan, Ochoa Toutouh, O’Reilly Shir, Bäck Automated Algorithm Quality-Diversity Decomposition Multi- Search Based Software Configuration and Design Optimization Objective Optimization: Engineering: challenges, Current Developments and opportunities and recent Future Opportunities applications utorial Room 2 T Stützle, López-Ibáñez Cully, Mouret, Doncieux Li, Zhang Ouni, Mkaouer Genetic improvement: Taking real- CMA-ES and Advanced Applications of Dynamic Towards a Green AI: world source code and improving Adaptation Mechanisms Parameter Control in Evolutionary solutions it using computational search Evolutionary Computation for an ecologically viable methods. artificial intelligence Haraldsson, Woodward, Wagner, utorial Room 3 T Brownlee, Alexander Akimoto, Hansen Papa Sánchez-Pi, Martí Dynamic Multi-objective Advanced Learning Lexicase Selection Optimization: Introduction, Classifier Systems Challenges, Applications and Future Directions utorial Room 4 T Helbig Browne Helmuth, La Cava Evolutionary Computation Evolutionary Computation Evolutionary Art and Push for Feature Selection and and Machine Learning in Design: Representation, Feature Construction Cryptology Fitness and Interaction utorial Room 5 T Xue, Zhang Picek, Jakobovic Machado Spector Introductory Mathematical Competitions Programming for EC utorial Room 6 T Competitions Room Shir p. 61 SWINGA – Swarm RWACMO – Real-World Applications of Continuous and SECDEF – Genetic and Intelligence Algorithms: Mixed-integer Optimization Evolutionary Computation Foundations, Perspectives in Defense, Security, and and Challenges Risk Management orkshop Room 2

W p. 51 p. 53 p. 56 SAEOPT – Surrogate-Assisted Evolutionary Optimisation EAPWU – Evolutionary Algorithms for Problems with Uncertainty orkshop Room 3

W p. 51 p. 56 IWLCS – 24th International BENCHMARK– AABOH – Analysing Algorithmic Behaviour of Workshop on Learning Benchmarking and Optimisation Heuristics Classifier Systems Reproducibility/Replicability orkshop Room 4

W p. 52 p. 53 p. 54 ECDM – Evolutionary EVOGRAPH – 2nd Workshop NEWK – Neuroevolution at Work Computation and Decision on Evolutionary Data Making Mining and Optimization over Graphs orkshop Room 5

W p. 52 p. 54 p. 55

Tutorials Workshops Competitions Schedule 31

Parallel Sessions, Monday, July 12 through Wednesday, July 14 (all times CEST=GMT+2/UTC+2)

Monday Monday Tuesday Tuesday Wednesday Wednesday July 12 July 12 July 13 July 13 July 14 July 14 10:20-11:40 14:20-15:40 12:20-13:40 14:20-15:40 10:20-11:40 12:20-13:40 RWA 1 - BP ECOM 2 - BP EML 1 + EMO 2 - BP SBSE 1 GECH 3 + NE 2 - BP p. 81 p. 83 p. 85 p. 88 p. 89 CS 1 - BP GP 1 - BP GA 2 - BP ACO-SI 3 + ENUM 2 ENUM 3 ENUM 1 + THEORY 1 - BP p. 81 p. 83 p. 85 p. 87 p. 90 p. 91 HUMIES RWA 2 RWA 3 RWA 4 RWA 5

p. 60 p. 85 p. 88 p. 90 p. 91 ECOM 1 ACO-SI 1 ACO-SI 2 ECOM 3 THEORY 2 THEORY 3

p. 81 p. 83 p. 84 p. 87 p. 90 p. 91 ECiP 1 ECiP 2 CS 2 CS 3 CS 4

p. 70 p. 70 p. 87 p. 89 p. 92 GECH 1 GECH 2 GP 2 GP 3 GECH 4 + IMPACT

p. 80 p. 82 p. 86 p. 88 p. 92 HOP 1 HOP 2 HOP 3 EML 2 EML 3

p. 80 p. 82 p. 84 p. 86 p. 89 NE 1 GA 1 EMO 1 NE 3 EMO 3 GA 3

p. 80 p. 82 p. 84 p. 86 p. 91 p. 92

Sessions with best HUMIES ECiP HOP paper nominees 32 Schedule

Track List and Abbreviations

ACO-SI: Ant Colony Optimization and Swarm Intelligence CS: Complex Systems (Artificial Life / Artificial Immune Systems / Generative and Developmental Systems / Evolutionary Robotics / Evolvable Hardware) ECiP: Evolutionary Computation in Practice ECOM: Evolutionary Combinatorial Optimization and Metaheuristics EML: Evolutionary Machine Learning EMO: Evolutionary Multiobjective Optimization ENUM: Evolutionary Numerical Optimization GA: Genetic Algorithms GECH: General Evolutionary Computation and Hybrids GP: Genetic Programming HUMIES: Annual “Humies” Awards For Human-Competitive Results HOP: Hot Off the Press LBA: Late-breaking Abstract NE: Neuroevolution RWA: Real World Applications SBSE: Search-Based Software Engineering THEORY: Theory Keynotes 34 Keynotes

GECCO KEYNOTE Reverse-engineering Core Common Sense with the Tools of Monday, July 12, 12:20-13:40 Probabilistic Programs, Game-style Simulation Engines, and Inductive Program Synthesis Joshua Tenenbaum, Massachusetts Institute of Technology, United States

None of today’s AI systems or approaches comes anywhere close to capturing the common sense of a toddler, or even a 3-month old infant. I will talk about some of the challenges facing conventional machine learning paradigms, such as end-to-end unsupervised learning in deep networks and deep reinforcement learning, and discuss some initial, small steps we have taken with an alternative cognitively-inspired AI approach. This requires us to develop a different engineering toolset, based on probabilistic programs, game-style simulation pro- grams as general-purpose startup software (or “the game engine in the head”), and learning as programming (or “the child as hacker”).

Biosketch: Joshua Tenenbaum is the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences, and a member of the Computer Science and Artificial Intelligence Laboratory at MIT. He received his Ph.D. from MIT in 1999 and after a brief postdoc with the MIT AI Lab, he joined the Stanford University faculty as Assistant Professor of Psychology and (by courtesy) Computer Science. He returned to MIT as a faculty member in 2002. He currently serves as Associate Editor of the journal Cognitive Science, and he has been active on the program committees of the Neural Information Processing Systems (NIPS) and Cognitive Science (CogSci) conferences. In 2019, he was named a MacArthur Fellow.

GECCO KEYNOTE Statistical Physics and Statistical Inference Tuesday, July 13, 10:20-11:40 Marc Mézard, École Normale Supérieure, France

A major challenge of contemporary statistical inference is the large-scale limit, where one wants to discover the values of many hidden parameters, using large amount of data. In recent years, ideas from statistical physics of disordered systems have helped to develop new algo- rithms for important inference problems, ranging from community detection to compressed sensing, machine learning (notably neural networks), tomography and generalized linear regression. The talk will review these developments and explain how they can be used, to develop new types of algorithms and identify phase transitions.

Biosketch: Marc Mézard is a theoretical physicist. He received a PhD from École normale supérieure in Paris, did a post-doc in Rome, and became the head of the statistical physics group in Paris-Sud University. Since 2012 he is the director of École normale supérieure. His main field of research is statistical physics and its use in various branches of science — biology, economics and finance, information theory, computer science, statistics, signal processing. In recent years his research has focused on information processing in neural networks. He has received the Lars Onsager prize from the American Physical Society, the Humboldt-Gay-Lussac prize, the silver medal of CNRS and the Ampere prize of the French Academy of Science. He is a member of the European Academy of Science. Keynotes 35

SIGEVOKEYNOTE Why AI is Harder Than We Think Wednesday, July 14, 14:20-15:40 Melanie Mitchell, Santa Fe Institute, United States

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI Spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI Winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this talk I will discuss some fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I will also speculate on what is needed for the grand challenge of making AI systems more robust, general, and adaptable — in short, more intelligent.

Biosketch: Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

Tutorials 38 Tutorials

Introductory Tutorials

Model-Based Evolutionary Algorithms Saturday, July 10, 08:30-10:20 Dirk Thierens, University of Utrecht Tutorial Room 1 Peter Bosman, Centrum Wiskunde & Informatica (CWI)

Learning Classifier Systems: From Principles to Modern Systems Saturday, July 10, 08:30-10:20 Anthony Stein, University of Hohenheim Tutorial Room 3 Masaya Nakata, Yokohama National University

Hyper-heuristics Saturday, July 10, 08:30-10:20 Daniel Tauritz, Auburn University Tutorial Room 4 John Woodward, Queen Mary University of London

Replicability and Reproducibility in Evolutionary Optimization Saturday, July 10, 08:30-10:20 Luís Paquete, University of Coimbra Tutorial Room 5 Manuel López-Ibáñez, University of Málaga

Representations for Evolutionary Algorithms Saturday, July 10, 11:00-12:50 Franz Rothlauf, University of Mainz Tutorial Room 3

Recent Advances in Particle Swarm Optimization Analysis and Saturday, July 10, 11:00-12:50 Understanding 2021 Tutorial Room 4 Christopher Cleghorn, University of the Witwatersrand Andries Engelbrecht, University of Stellenbosch

A Gentle Introduction to Theory (for Non-Theoreticians) Saturday, July 10, 11:00-12:50 Benjamin Doerr, Ecole Polytechnique, Laboratoire d’Informatique (LIX) Tutorial Room 5

Runtime Analysis of Evolutionary Algorithms: Basic Introduction Saturday, July 10, 13:30-15:20 Per Kristian Lehre, University of Birmingham Tutorial Room 3 Pietro Oliveto, The University of Sheffield

Benchmarking: state-of-the-art and beyond Saturday, July 10, 13:30-15:20 Anne Auger, Inria Tutorial Room 4 Nikolaus Hansen, Inria

Genetic Programming A Tutorial Introduction Saturday, July 10, 16:00-17:50 Una-May O’Reilly, MIT CSAIL Tutorial Room 3 Erik Hemberg, MIT CSAIL, ALFA Group

Evolution of Neural Networks Saturday, July 10, 16:00-17:50 Risto Miikkulainen, The University of Texas at Austin, Cognizant AI Labs Tutorial Room 4

Theoretical Foundations of Evolutionary Computation for Beginners and Saturday, July 10, 16:00-17:50 Veterans Tutorial Room 5 Darrell Whitley, Colorado State University

Introductory Mathematical Programming for EC Sunday, July 11, 08:30-10:20 Ofer Shir, Tel-Hai College, The Galilee Research Institute - Migal Tutorial Room 6 Tutorials 39

Advanced Tutorials Statistical Analyses for Meta-heuristic Stochastic Optimization Algorithms Saturday, July 10, 11:00-12:50 Tome Eftimov, Jožef Stefan Institute Tutorial Room 1 Peter Korošec, Jožef Stefan Institute

Evolutionary Submodular Optimisation Saturday, July 10, 11:00-12:50 Aneta Neumann, The University of Adelaide Tutorial Room 2 Frank Neumann, The University of Adelaide Chao Qian, Nanjing University

Benchmarking Multiobjective Optimizers 2.0 Saturday, July 10, 13:30-15:20 Dimo Brockhoff, Inria; CMAP,Ecole Polytechnique, IP Paris Tutorial Room 1 Tea Tušar, Jozef Stefan Institute

Constraint-Handling Techniques used with Evolutionary Algorithms Saturday, July 10, 13:30-15:20 Carlos Coello Coello, CINVESTAV-IPN Tutorial Room 2

Evolutionary Multi- and Many-Objective Optimization: Methodologies, Saturday, July 10, 16:00-17:50 Applications and Demonstration Tutorial Room 2 Kalyanmoy Deb, Michigan State University Julian Blank, Michigan State University

Runtime Analysis of Population-based Evolutionary Algorithms Sunday, July 11, 08:30-10:20 Per Kristian Lehre, University of Birmingham Tutorial Room 1 Pietro Oliveto, The University of Sheffield

Automated Algorithm Configuration and Design Sunday, July 11, 08:30-10:20 Thomas Stützle, Université Libre de Bruxelles Tutorial Room 2 Manuel López-Ibáñez, University of Málaga, University of Manchester

Genetic improvement: Taking real-world source code and improving it Sunday, July 11, 08:30-10:20 using computational search methods. Tutorial Room 3 Saemundur Haraldsson, University of Stirling John Woodward, Queen Mary University of London Markus Wagner, The University of Adelaide Alexander Brownlee, University of Stirling Bradley Alexander, The University of Adelaide

Dynamic Multi-objective Optimization: Introduction, Challenges, Sunday, July 11, 08:30-10:20 Applications and Future Directions Tutorial Room 4 Marde Helbig, Griffith University

Recent Advances in Landscape Analysis for Optimisation and Learning Sunday, July 11, 11:00-12:50 Katherine Malan, University of South Africa Tutorial Room 1 Gabriela Ochoa, University of Stirling

Quality-Diversity Optimization Sunday, July 11, 11:00-12:50 Antoine Cully, Imperial College Tutorial Room 2 Jean-Baptiste Mouret, Inria Stéphane Doncieux, ISIR, Sorbonne Université 40 Tutorials

CMA-ES and Advanced Adaptation Mechanisms Sunday, July 11, 11:00-12:50 Youhei Akimoto, University of Tsukuba, RIKEN AIP Tutorial Room 3 Nikolaus Hansen, Inria, Ecole Polytechnique

Advanced Learning Classifier Systems Sunday, July 11, 11:00-12:50 Will Browne, Queensland University of Technology Tutorial Room 4

Coevolutionary Computation for Adversarial Deep Learning Sunday, July 11, 13:30-15:20 Jamal Toutouh, Massachusetts Inst. of Technology, University of Málaga Tutorial Room 1 Una-May O’Reilly, Massachusetts Inst. of Technology

Decomposition Multi-Objective Optimization: Current Developments and Sunday, July 11, 13:30-15:20 Future Opportunities Tutorial Room 2 Ke Li, University of Exeter Qingfu Zhang, City University of Hong Kong

Lexicase Selection Sunday, July 11, 13:30-15:20 Thomas Helmuth, Hamilton College Tutorial Room 4 William La Cava, University of Pennsylvania

Sequential Experimentation by Evolutionary Algorithms Sunday, July 11, 16:00-17:50 Ofer Shir, Tel-Hai College, The Galilee Research Institute - Migal Tutorial Room 1 Thomas Bäck, Leiden University

Specialized Tutorials

Evolutionary Computation and Evolutionary Deep Learning for Image Saturday, July 10, 08:30-10:20 Analysis, Signal Processing and Pattern Recognition Tutorial Room 2 Mengjie Zhang, Victoria University of Wellington Stefano Cagnoni, University of Parma

Evolutionary Computation for Feature Selection and Feature Construction Sunday, July 11, 08:30-10:20 Bing Xue, Victoria University of Wellington Tutorial Room 5 Mengjie Zhang, Victoria University of Wellington

Evolutionary Computation and Machine Learning in Cryptology Sunday, July 11, 11:00-12:50 Stjepan Picek, Delft University of Technology Tutorial Room 5 Domagoj Jakobovic, za

Applications of Dynamic Parameter Control in Evolutionary Computation Sunday, July 11, 13:30-15:20 Gregor Papa, Jožef Stefan Institute Tutorial Room 3

Evolutionary Art and Design: Representation, Fitness and Interaction Sunday, July 11, 13:30-15:20 Penousal Machado, University of Coimbra, CISUC Tutorial Room 5

Search Based Software Engineering: challenges, opportunities and recent Sunday, July 11, 16:00-17:50 applications Tutorial Room 2 Ali Ouni, Ecole de Technologie Superieure Mohamed Wiem Mkaouer, Rochester Institute of Technology Tutorials 41

Towards a Green AI: Evolutionary solutions for an ecologically viable Sunday, July 11, 16:00-17:50 artificial intelligence Tutorial Room 3 Nayat Sánchez-Pi, Inria Chile Research Center Luis Martí, Inria Chile Research Center

Push Sunday, July 11, 16:00-17:50 Lee Spector, Hampshire College / Amherst College, University of Tutorial Room 5 Massachusetts Amherst

Workshops, Late-breaking Abstracts, and Women@GECCO 44 Workshops, Late-breaking Abstracts, and Women@GECCO

IAM – 6th Workshop on Industrial Applications of Metaheuristics

Organizers: Silvino Fernandez Alzueta (ArcelorMittal); Thomas Stützle (Université Libre de Bruxelles); Pablo Valledor (ArcelorMittal) Time: Saturday, July 10, 08:30-12:50, Workshop Room 3

Opening Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stützle Advanced Mine Optimisation under Uncertainty Using Evolution William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor Daniel Hein, Daniel Labisch A heuristic approach to feasibility verification for truck loading Vinicius Gandra, Hatice Çalik, Tony Wauters, Greet Vanden Berghe

Simulation-based Scheduling of a Large-scale Industrial Formulation Plant Using a Heuristics-assisted Genetic Algorithm Christian Klanke, Dominik Bleidorn, Christian Koslowski, Christian Sonntag, Sebastian Engell

Determining a consistent experimental setup for benchmarking and optimizing databases Moisés Silva-Muñoz, Gonzalo Calderon, Alberto Franzin, Hugues Bersini

Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge Anna Kononova, Ofer Shir, Teus Tukker, Pierluigi Frisco, Shutong Zeng, Thomas Bäck

Multi Tree Operators for Genetic Programming to Identify Optimal Energy Flow Controllers Kathrin Kefer, Roland Hanghofer, Patrick Kefer, Markus Stöger, Bernd Hofer, Michael Affenzeller, Stephan Winkler A Multi-Objective Genetic Algorithm for Jacket Optimization Jan Burak, Ole Mengshoel

Using Grammatical Evolution for Modelling Energy Consumption on a Computer Numerical Control Machine Samuel Carvalho, Joe Sullivan, Douglas Dias, Enrique Naredo, Conor Ryan

Multi-Objective Evolutionary Product Bundling: A Case Study Okan Tunalı, Ahmet Tu˘grul Bayrak, Víctor Sanchez-Anguix, Reyhan Aydo˘gan

Closing Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stützle

ECPERM – Evolutionary Computation for Permutation Problems

Organizers: Marco Baioletti (University of Perugia); Josu Ceberio (University of the Basque Country); John McCall (Smart Data Technologies Centre); Alfredo Milani (University of Perugia); Valentino Santucci (University for Foreigners of Perugia) Time: Saturday, July 10, 08:30-12:50, Workshop Room 4

Uniform crossovers for permutations Ekhine Irurozki Workshops, Late-breaking Abstracts, and Women@GECCO 45

Towards the Landscape Rotation as a Perturbation Strategy on the Quadratic Assignment Problem Joan Alza, Mark Bartlett, Josu Ceberio, John McCall Generating Instances with Performance Differences for More Than Just Two Algorithms Jakob Bossek, Markus Wagner

On the symmetry of the Quadratic Assignment Problem through Elementary Landscape Decomposition Xabier Benavides, Josu Ceberio, Leticia Hernando Discussion on "Instances and landscapes for permutation problems" Marco Baioletti, Josu Ceberio, John McCall, Alfredo Milani, Valentino Santucci Hybrid Linkage Learning for Permutation Optimization with Gene-pool Optimal Mixing Evolutionary Algorithms Michal Przewozniczek, Marcin Komarnicki, Peter Bosman, Dirk Thierens, Bartosz Frej, Ngoc Luong

Exploratory Analysis Of The Monte Carlo Tree Search For Solving The Linear Ordering Problem Andoni Irazusta Garmendia, Josu Ceberio, Alexander Mendiburu Discussion on “Search methods for permutation problems” Marco Baioletti, Josu Ceberio, John McCall, Alfredo Milani, Valentino Santucci Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions Thomas Weise, Xinlu Li, Yan Chen, Zhize Wu An Empirical Evaluation of Permutation-Based Policies for Stochastic RCPSP Olivier Regnier-Coudert, Guillaume Povéda

Discussion on “Scheduling problems” Marco Baioletti, Josu Ceberio, John McCall, Alfredo Milani, Valentino Santucci

LAHS – Landscape-Aware Heuristic Search

Organizers: Arnaud Liefooghe (Univ. Lille, Inria Lille - Nord Europe); Katherine M. Malan (University of South Africa); Gabriela Ochoa (University of Stirling); Nadarajen Veerapen (Université de Lille; University of Lille, Central Lille); Sebastien Verel (Université du Littoral Côte d’Opale) Time: Saturday, July 10, 08:30-12:50, Workshop Room 5

Opening Nadarajen Veerapen, Katherine Malan, Arnaud Liefooghe, Sébastien Verel, Gabriela Ochoa

Dissipative Polynomials William Langdon, Justyna Petke, David Clark

Investigating the Landscape of a Hybrid Local Search Approach for a Timetabling Problem Thomas Feutrier, Marie-Éléonore Kessaci, Nadarajen Veerapen

Understanding Parameter Spaces using Local Optima Networks: A Case Study on Particle Swarm Optimization Christopher Cleghorn, Gabriela Ochoa

Analysing the Loss Landscape Features of Generative Adversarial Networks Jarrod Moses, Katherine Malan, Anna Bosman 46 Workshops, Late-breaking Abstracts, and Women@GECCO

Dynamic Landscape Analysis for Open-Ended Stacking Bernhard Werth, Johannes Karder, Andreas Beham, Stefan Wagner

Towards Population-based Fitness Landscape Analysis Using Local Optima Networks Melike Karatas, Ozgur Akman, Jonathan Fieldsend

Open Discussion Nadarajen Veerapen, Katherine Malan, Arnaud Liefooghe, Sébastien Verel, Gabriela Ochoa

VIZGEC – Visualisation Methods in Genetic and Evolutionary Computation

Organizers: Richard Everson (University of Exeter); Neil Vaughan (University of Exeter, Royal Academy of Engineering); David Walker (University of Plymouth); Rui Wang (Uber AI Labs) Time: Saturday, July 10, 11:00-12:50, Workshop Room 1

Using Parallel Coordinates to Explore Solution Spaces Neil Urquhart

Visualizing fitnesses and constraint violations in single-objective optimization Tomofumi Kitamura, Alex Fukunaga

Many-objective Population Visualisation with Geons Marius Varga, Swen Gaudl, David Walker

BBOB – Black Box Optimization Benchmarking

Organizers: Anne Auger (INRIA; CMAP,Ecole Polytechnique); Peter A.N. Bosman (Centre for Mathematics and Computer Science, Delft University of Technology); Dimo Brockhoff (INRIA Saclay - Ile-de- France; CMAP,Ecole Polytechnique); Tobias Glasmachers (Ruhr-University Bochum); Nikolaus Hansen (Inria, Ecole polytechnique); Petr Pošík (Czech Technical University in Prague); Tea Tusar (Jozef Stefan Institute) Time: Saturday, July 10, 11:00-12:50, Workshop Room 2

The BBOBies: A Short Introduction to BBOB/COCO Anne Auger, Peter Bosman, Dimo Brockhoff, Tobias Glasmachers, Nikolaus Hansen, Petr Posik, Tea Tusar

Benchmarking SHADE algorithm enhanced with model based optimization on the BBOB noiseless testbed Michał Okulewicz, Mateusz Zaborski DMS and MultiGLODS: Black-Box Optimization Benchmarking of Two Direct Search Methods on the Biobjective bbob-biobj Test Suite Dimo Brockhoff, Baptiste Plaquevent-Jourdain, Anne Auger, Nikolaus Hansen

General discussion about the future of BBOB/COCO Anne Auger, Peter Bosman, Dimo Brockhoff, Tobias Glasmachers, Nikolaus Hansen, Petr Posik, Tea Tusar Workshops, Late-breaking Abstracts, and Women@GECCO 47

ECADA – 11th Workshop on Evolutionary Computation for the Automated Design of Algorithms

Organizers: Manuel López-Ibáñez (University of Málaga, University of Manchester); Daniel R. Tauritz (Auburn University); John Woodward (Queen Mary University of London) Time: Saturday, July 10, 13:30-17:50, Workshop Room 1

Opening Daniel Tauritz, John Woodward, Manuel López-Ibáñez

A selection hyperheuristic guided by Thompson Sampling for numerical optimization Marcella Scoczynski, Diego Oliva, Erick Rodriguez-Esparza, Myriam Delgado, Ricardo Lüders, Mohamed El Yafrani, Luiz Ledo, Mohamed Abd Elaziz, Marco Perez-Cisnero Towards Large Scale Automated Algorithm Designby Integrating Modular Benchmarking Frameworks Amine Aziz-Alaoui, Carola Doerr, Johann Dreo Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Baeck

Automated Design of Accurate and Robust Image Classifiers with Brain Programming Gerardo Ibarra-Vazquez, Gustavo Olague, Cesar Puente, Mariana Chan-Ley, Carlos Soubervielle-Montalvo

Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network for Molecular Property Prediction Yingfang Yuan, Wenjun Wang, Wei Pang

Understanding the Search Space of Automated Machine Learning Algorithms Gisele Pappa

Open Discussion Daniel Tauritz, John Woodward, Manuel López-Ibáñez

Closing Daniel Tauritz, John Woodward, Manuel López-Ibáñez

EVORL – Evolutionary Reinforcement Learning Workshop

Organizers: Alex Coninx (Sorbonne Universite; CNRS, ISIR); Antoine Cully (Imperial College); Adam Gaier (Autodesk Research); Giuseppe Paolo (Sorbonne University, Softbank robotics europe) Time: Saturday, July 10, 13:30-17:50, Workshop Room 2

Opening of EVORL 1 Giuseppe Paolo, Alex Coninx, Antoine Cully, Adam Gaier

Using Deep Q-Network for Selection Hyper-Heuristics Augusto Dantas, Alexander do Rego, Aurora Pozo

Using Reinforcement Learning for Tuning Genetic Algorithms Jose Quevedo, Marwan Abdelatti, Farhad Imani, Manbir Sodhi 48 Workshops, Late-breaking Abstracts, and Women@GECCO

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme Léni Le Goff, Emma Hart Scaling Deep Neuroevolution to Complex Reinforcement Learning Tasks Sebastian Risi Closing of EVORL 1 Giuseppe Paolo, Alex Coninx, Antoine Cully, Adam Gaier

Opening of EVORL 2 Giuseppe Paolo, Alex Coninx, Antoine Cully, Adam Gaier

AI-Generating Algorithms: A Unique Opportunity for the Evolutionary RL Community Jeff Clune Coordinate Ascent MORE With Adaptive Entropy Control for Population-Based Regret Minimization Maximilian Hüttenrauch, Gerhard Neumann Novelty and MCTS Hendrik Baier, Michael Kaisers Evolutionary Reinforcement Learning for Sparse Rewards Shibei Zhu, Francesco Belardinelli, Borja González León

Closing of EVORL 2 Giuseppe Paolo, Alex Coninx, Antoine Cully, Adam Gaier

PDEIM – Parallel and Distributed Evolutionary Inspired Methods

Organizers: Ivanoe De Falco (ICAR-CNR); Antonio Della Cioppa (Natural Computation Lab - DIEM, University of Salerno); Umberto Scafuri (ICAR-CNR); Ernesto Tarantino (ICAR - CNR) Time: Saturday, July 10, 13:30-15:20, Workshop Room 3

Solving QUBO with GPU Parallel MOPSO Noriyuki Fujimoto, Kouki Nanai

A Partially Asynchronous Global Parallel Genetic Algorithm Darren Chitty

Island Model in ActoDatA: an actor-based Implementation of a classical Distributed Evolutionary Computation Paradigm Giuseppe Petrosino, Federico Bergenti, Gianfranco Lombardo, Monica Mordonini, Agostino Poggi, Michele Tomaiuolo, Stefano Cagnoni

An operation to promote diversity in Evolutionary Algorithms in a Dynamic Hybrid Island Model Grasiele Duarte, Beatriz Lima Conduit: A C++ Library for Best-Effort High Performance Computing Matthew Moreno, Santiago Rodriguez Papa, Charles Ofria Workshops, Late-breaking Abstracts, and Women@GECCO 49

STUDENT – Student Workshop

Organizers: Francisco Chicano (University of Malaga); Katya Rodríguez (IIMAS-UNAM); Sara Tari (Université du Littoral Côte d’Opale) Time: Saturday, July 10, 13:30-17:50, Workshop Room 4

CLAHC - Custom Late Acceptance Hill Climbing: first results on TSP Sylvain Clay, Lucien Mousin, Nadarajen Veerapen, Laetitia Jourdan

Automated Parameter Choice with Exploratory Landscape Analysis and Machine Learning Maxim Pikalov, Vladimir Mironovich The Lower Bounds on the Runtime of the $(1 + (\lambda, \lambda))$~GA on the Minimum Spanning Tree Problem Matvey Shnytkin, Denis Antipov

A Parallel Genetic Algorithm to Speed Up the Resolution of the Algorithm Selection Problem Alejandro Marrero, Eduardo Segredo, Coromoto Leon

Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification Anish Thite, Mohan Dodda, Alex Liu, Pulak Agarwal, Jason Zutty

Negative Learning Ant Colony Optimization for the Minimum Positive Influence Dominating Set Problem Albert López Serrano, Teddy Nurcahyadi, Salim Bouamama, Christian Blum

Population network structure impacts genetic algorithm optimisation performance Aymeric Vié

DTEO – 4th GECCO Workshop on Decomposition Techniques in Evolutionary Optimization

Organizers: Bilel Derbel (Univ. Lille, Inria Lille - Nord Europe); Xiaodong Li (RMIT University); Ke Li (University of Exeter); Saúl Zapotecas-Martínez (Universdad Autónoma Metropolitana); Qingfu Zhang (City University of Hong Kong, City University of Hong Kong Shenzhen Research Institute) Time: Saturday, July 10, 13:30-15:20, Workshop Room 5

Welcome & Opening Ke Li The Bee-Benders Hybrid Algorithm with application to Transmission Expansion Planning Cameron MacRae, Melih Ozlen, Andreas Ernst Population-Based Coordinate Descent Algorithm with Majority Voting Davood Zaman Farsa, Azam Asilian Bidgoli, Ehsan Rokhsatyazdi, Shahryar Rahnamayan

An Abstract Interface for Large-Scale Continuous Optimization Decomposition Methods Rodolfo Lopes, Rodrigo Silva, Alan Freitas

Closing Remarks Ke Li 50 Workshops, Late-breaking Abstracts, and Women@GECCO

EAHPC – Evolutionary Algorithms and HPC

Organizers: Mark Coletti (Oak Ridge National Laboratory); Kenneth De Jong (George Mason University); Robert M. Patton (Oak Ridge National Laboratory); Catherine Schuman (Oak Ridge National Laboratory); Eric Scott (George Mason University, MITRE) Time: Saturday, July 10, 16:00-17:50, Workshop Room 3

Opening Remarks Mark Coletti, Robert Patton, Catherine Schuman, Eric Scott, Kenneth De Jong

An Efficient Fault-tolerant Communication Algorithm for Population-based Metaheuristics Amanda Dufek, Douglas Augusto, Helio Barbosa, Pedro Dias, Jack Deslippe

Improving the Scalability of Distributed Neuroevolution Using Modular Congruence Class Generated Innovation Numbers Joshua Karns, Travis Desell Generating Combinations on the GPU and its Application to the K-Subset Sum Victor Parque

X-Aevol: GPU Implementation of an Evolutionary Experimentation Simulator Laurent Turpin, Jonathan Rouzaud-Cornabas, Thierry Gautier Discussion Mark Coletti, Robert Patton, Catherine Schuman, Eric Scott, Kenneth De Jong

Closing Remarks Mark Coletti, Robert Patton, Catherine Schuman, Eric Scott, Kenneth De Jong

EVOSOFT – Evolutionary Computation Software Systems

Organizers: Michael Affenzeller (University of Applied Science Upper Austria; Institute for Formal Models and Verification,Johannes Keppler University Linz); Stefan Wagner (University of Applied Sciences Upper Austria) Time: Saturday, July 10, 16:00-17:50, Workshop Room 5

Welcome & Opening Stefan Wagner

Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics Johann Dreo, Arnaud Liefooghe, Sébastien Verel, Marc Schoenauer, Juan Merelo, Alexandre Quemy, Benjamin Bouvier, Jan Gmys

Component-Based Design of Multi-Objective Evolutionary Algorithms Using the Tigon Optimization Library Joao Duro, Daniel Oara, Ambuj Sriwastava, Yiming Yan, Shaul Salomon, Robin Purshouse

EBIC.JL - an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia Paweł Renc, Patryk Orzechowski, Jarosław W ˛as,Aleksander Byrski, Jason Moore

AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms Kory Becker, Justin Gottschlich Workshops, Late-breaking Abstracts, and Women@GECCO 51

Closing Remarks Michael Affenzeller

SWINGA – Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges

Organizers: Swagatam Das (Indian Statistical Institute); Roman Šenkeˇrík (Tomas Bata University); Ivan Zelinka (VSB-TU Ostrava) Time: Sunday, July 11, 08:30-10:20, Workshop Room 2

Welcome & Opening Roman Šenkeˇrík Explaining SOMA: the Relation of Stochastic Perturbation to Population Diversity and Parameter Space Coverage Michal Pluhacek, Anezka Kazikova, Tomas Kadavy, Adam Viktorin, Roman Šenkeˇrík

Self-organizing Migrating Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control Tomas Kadavy, Michal Pluhacek, Adam Viktorin, Roman Šenkeˇrík

A Population-based Automatic Clustering Algorithm for Image Segmentation Seyed Jalaleddin Mousavirad, Gerald Schaefer, Mahshid Helali Moghadam, Mehrdad Saadatmand, Mahdi Pedram HCS-BBD: An Effective Population-Based Approach for Multi-Level Thresholding Seyed Jalaleddin Mousavirad, Gerald Schaefer, Diego Oliva, Salvador Hinojosa

A Differential Particle Scheme and its Application to PID Parameter Tuning of an Inverted Pendulum Victor Parque

Closing Roman Šenkeˇrík

SAEOPT – Surrogate-Assisted Evolutionary Optimisation

Organizers: Richard Everson (University of Exeter); Jonathan Edward Fieldsend (University of Exeter); Yaochu Jin (University of Surrey); Alma Rahat (Swansea University); Handing Wang (Xidian University) Time: Sunday, July 11, 08:30-12:50, Workshop Room 3

Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT Thomas Bartz-Beielstein How Bayesian Should Bayesian Optimisation Be? George De Ath, Richard Everson, Jonathan Fieldsend

Preferential Bayesian optimisation with Skew Gaussian Processes Alessio Benavoli, Dario Azzimonti, Dario Piga

Black-box Mixed-Variable Optimisation Using a Surrogate Model that Satisfies Integer Constraints Laurens Bliek, Arthur Guijt, Sicco Verwer, Mathijs de Weerdt 52 Workshops, Late-breaking Abstracts, and Women@GECCO

A Two-Phase Surrogate Approach for High-Dimensional Constrained Discrete Multi-Objective Optimization Rommel Regis

Augmenting High-dimensional Nonlinear Optimization with Conditional GANs Pouya Rezazadeh Kalehbasti, Michael Lepech, Samarpreet Singh Pandher

IWLCS – 24th International Workshop on Learning Classifier Systems

Organizers: Michael Heider (Universität Augsburg); David Pätzel (University of Augsburg); Alexander Wagner (University of Hohenheim) Time: Sunday, July 11, 08:30-10:20, Workshop Room 4

Welcome Note David Pätzel, Alexander Wagner, Michael Heider

An Overview of LCS Research from 2020 to 2021 David Pätzel, Michael Heider, Alexander Wagner

An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS Tim Hansmeier, Marco Platzner A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies Jordan Bishop, Marcus Gallagher, Will Browne

General Discussion David Pätzel, Alexander Wagner, Michael Heider

Closing David Pätzel, Alexander Wagner, Michael Heider

ECDM – Evolutionary Computation and Decision Making

Organizers: Richard Allmendinger (University of Manchester); Tinkle Chugh (University of Exeter); Jussi Hakanen (University of Jyvaskyla) Time: Sunday, July 11, 08:30-10:20, Workshop Room 5

Opening Tinkle Chugh, Richard Allmendinger, Jussi Hakanen

A Divide and Conquer Approach for Web Services Location Allocation Problem Harshal Tupsamudre, Saket Saurabh, Arun Ramamurthy, Mangesh Gharote, Sachin Lodha

Model Learning with Personalized Interpretability Estimation (ML-PIE) Marco Virgolin, Andrea De Lorenzo, Francesca Randone, Eric Medvet, Mattias Wahde

Break Tinkle Chugh, Richard Allmendinger, Jussi Hakanen

Workshop Keynote: Multiobjective Optimization and Decision Analysis from an Explainable AI Perspective Michael Emmerich Workshops, Late-breaking Abstracts, and Women@GECCO 53

Closing Tinkle Chugh, Richard Allmendinger, Jussi Hakanen

RWACMO – Real-World Applications of Continuous and Mixed-integer Optimization

Organizers: Kazuhisa Chiba (The University of Electro-Communications); Akira Oyama (Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency); Pramudita Satria Palar (Institut Teknologi Bandung); Koji Shimoyama (Tohoku University); Hemant K. Singh (University of New South Wales) Time: Sunday, July 11, 11:00-15:20, Workshop Room 2

Opening, recap of previous RWACMOs Pramudita Satria Palar Workshop Keynote: Recent Advancements on Optimization under Severe Uncertainty in Astrodynamics Massimiliano Vasile A Matheuristic Approach for Finding Effective Base Locations and Team Configurations for North West Air Ambulance Burak Boyaci, Muhammad Ali Nayeem, Ahmed Kheiri

Evolutionary Algorithms in High-dimensional Radio Access Network Optimization Dmitriy Semenchikov, Anna Filippova, Dmitriy Volf, Nikolai Kovrizhnykh, Maxim Mironov, Zou Jinying, Luo Ronghui, Zhu Yuanming, Huang Wei, Chai Dapeng

Workshop Keynote: Knowledge Discovery using Multidisciplinary Design Optimization and Data Mining Takehisa Kohira Black-box adversarial attacks using Evolution Strategies Hao Qiu, Leonardo Custode, Giovanni Iacca House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study Fateme Azimlu, Shahryar Rahnamayan, Masoud Makrehchi

Project Portfolio Selection with Defense Capability Options Kyle Harrison, Saber Elsayed, Ruhul Sarker, Ivan Garanovich, Terence Weir, Sharon Boswell

Closing Remarks Kazuhisa Chiba

BENCHMARK – Benchmarking and Reproducibility/Replicability

Organizers: Juergen Branke (University of Warwick); Carola Doerr (CNRS, Sorbonne University); Tome Eftimov (Jožef Stefan Institute, Stanford University); Pascal Kerschke (University of Münster); Manuel López-Ibáñez (University of Málaga, University of Manchester); Boris Naujoks (TH Köln - University of Applied Sciences) Time: Sunday, July 11, 11:00-12:50, Workshop Room 4

Welcome & Opening Juergen Branke, Carola Doerr, Tome Eftimov, Pascal Kerschke, Manuel López-Ibáñez, Boris Naujoks, Luís Paquete, Vanessa Volz, Thomas Weise 54 Workshops, Late-breaking Abstracts, and Women@GECCO

Workshop Keynote: Relevance of Benchmarking for Industry or “What does it take for an algorithm to be applied in industrial contexts? Bernhard Sendhoff Workshop Keynote: Benchmarking in Robotics Emma Hart, Gusz Eiben Open Discussion Juergen Branke

EVOGRAPH – 2nd Workshop on Evolutionary Data Mining and Optimization over Graphs

Organizers: David Camacho (Universidad Politécnica de Madrid); Javier Del Ser (University of the Basque Country (UPV/EHU)); Eneko Osaba (Tecnalia Research & Innovation) Time: Sunday, July 11, 11:00-12:50, Workshop Room 5

Welcome & Opening Eneko Osaba Focusing on the Hybrid Quantum Computing - Tabu Search Algorithm: new results on the Asymmetric Salesman Problem Eneko Osaba, Esther Villar-Rodriguez, Izaskun Oregi, Aitor Moreno-Fernandez-de-Leceta

Graph-Aware Evolutionary Algorithms for Influence Maximization Kateryna Konotopska, Giovanni Iacca

Closing Eneko Osaba

AABOH – Analysing Algorithmic Behaviour of Optimisation Heuristics

Organizers: Thomas Bäck (Leiden University); Fabio Caraffini (De Montfort University); Carola Doerr (CNRS, Sorbonne University); Anna Kononova (Leiden University); Hao Wang (Sorbonne Université, CNRS); Thomas Weise (Hefei University) Time: Sunday, July 11, 13:30-17:50, Workshop Room 4

Opening Anna Kononova On the Genotype Compression and Expansion for Evolutionary Algorithms in the Continuous Domain Lucija Planini´c,Marko Ðurasevi´c, Luca Mariot, Domagoj Jakobovi´c, Stjepan Picek, Carlos Coello Coello

Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution Rick Boks, Anna Kononova, Hao Wang

Benchmark Generator for TD Mk Landscapes Tobias van Driessel, Dirk Thierens Design of Large-Scale Metaheuristic Component Studies Helena Stegherr, Michael Heider, Leopold Luley, Jörg Hähner Workshops, Late-breaking Abstracts, and Women@GECCO 55

Emergence of Structural Bias in Differential Evolution Bas van Stein, Fabio Caraffini, Anna Kononova Is there Anisotropy in Structural Bias? Diederick Vermetten, Anna Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck

Introduction Anna Kononova On the Bias of Point Distributions on the Pareto front in Indicator-Based Multiobjective Optimization Michael Emmerich Patterns of Population Dynamics in Differential Evolution: Merging Theoretical and Empirical Results Daniela Zaharie Applications of Bayesian Optimization in Natural Language Processing Lingpeng Kong

NEWK – Neuroevolution at Work

Organizers: Ivanoe De Falco (ICAR-CNR); Antonio Della Cioppa (Natural Computation Lab - DIEM, University of Salerno); Umberto Scafuri (ICAR-CNR); Ernesto Tarantino (ICAR - CNR) Time: Sunday, July 11, 13:30-17:50, Workshop Room 5

On the Effects of Pruning on Evolved Neural Controllers for Soft Robots Giorgia Nadizar, Eric Medvet, Felice Andrea Pellegrino, Marco Zullich, Stefano Nichele

Prediction of Personalized Blood Glucose Levels in Type 1 Diabetic Patients using a Neuroevolution Approach Ivanoe De Falco, Antonio Della Cioppa, Angelo Marcelli, Umberto Scafuri, Luca Stellaccio, Ernesto Tarantino

Pareto-Optimal Progressive Neural Architecture Search Eugenio Lomurno, Stefano Samele, Matteo Matteucci, Danilo Ardagna

Hybrid Encodings for Neuroevolution of Convolutional Neural Networks: A Case Study Gustavo-Adolfo Vargas-Hákim, Efrén Mezura-Montes, Héctor-Gabriel Acosta-Mesa

Behavior-based Neuroevolutionary Training in Reinforcement Learning Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, A. E. Eiben

Evolving Neural Selection with Adaptive Regularization Li Ding, Lee Spector

Neuroevolution of Recurrent Neural Networks for Time Series Forecasting of Coal-Fired Power Plant Operating Parameters Zimeng Lyu, Shuchita Patwardhan, David Stadem, James Langfeld, Steve Benson, Seth Thoelke, Travis Desell

Neuro-evolution at Work in Neuroscience and Robotics Eric Scott, Kenneth De Jong 56 Workshops, Late-breaking Abstracts, and Women@GECCO

SECDEF – Genetic and Evolutionary Computation in Defense, Security, and Risk Management

Organizers: Riyad Alshammari (King Saud bin Abdulaziz University for Health Sciences); Erik Hemberg (MIT CSAIL, ALFA Group); Tokunbo Makanju (New York Institute of Technology) Time: Sunday, July 11, 16:00-17:50, Workshop Room 2

Opening Remarks Erik Hemberg

Cyber Threat Hunting With Adaptive Cyber Defense Systems John Emanuello Competitive Coevolution for Defense and Security: Elo-Based Similar-Strength Opponent Sampling Sean Harris, Daniel Tauritz Simulating a Logistics Enterprise Using an Asymmetrical Wargame Simulation with Soar Reinforcement Learning and Coevolutionary Algorithms Ying Zhao, Erik Hemberg, Nate Derbinsky, Gabino Mata, Una-May O’Reilly

Workshop Keynote: Evolutionary Computation Applied to Network Security Nur Zincir-Heywood

Deceiving Neural Source Code Classifiers: Finding Adversarial Examples with Grammatical Evolution Claudio Ferretti, Martina Saletta Multi-objective Evolutionary Algorithms for Distributed Tactical Control of Heterogeneous Agents Rahul Dubey, Sushil Louis

Closing Remarks Erik Hemberg

EAPWU – Evolutionary Algorithms for Problems with Uncertainty

Organizers: Khulood Alyahya (Exeter University); Juergen Branke (University of Warwick); Tinkle Chugh (University of Exeter); Jonathan Edward Fieldsend (University of Exeter) Time: Sunday, July 11, 16:00-17:50, Workshop Room 3

RARE: Evolutionary Feature Engineering for Rare-variant Bin Discovery Satvik Dasariraju, Ryan Urbanowicz

Maximising Hypervolume and Minimising $\epsilon$-Indicators using Bayesian Optimisation over Sets Tinkle Chugh, Manuel López-Ibáñez

A New Acquisition Function for Robust Bayesian Optimization of Unconstrained Problems Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck

Workshop Keynote: Uncertainty Quantification in Complex Numerical Models Peter Challenor Workshops, Late-breaking Abstracts, and Women@GECCO 57

Late-breaking Abstracts

Organizer: Federica Sarro (University College London)

Time: Monday, July 12, 18:00-20:00 and Tuesday, July 13, 09:00-10:20, Poster Room 1 & 2 (Gather). LBAs will be presented together with Posters (p. 93) and Competition Entries (p. 67).

A new Hybrid Evolutionary Algorithm for Dial-A-Ride Problems Sonia Nasri, Hend Bouziri, Wassila Aggoune-Matalaa

A software library for archiving nondominated points Duarte Dias, Alexandre Jesus, Luis Paquete Algorithm Selection using Transfer Learning Niranjana Deshpande, Naveen Sharma

An Improved Predictor of Daily Stock Index Based on a Genetic Filter Dong-Hee Cho, Seung-Hyun Moon, Yong-Hyuk Kim

An Interactive Tool for Enhancing Hospital Capacity Predictions Using an Epidemiological Model Finley Gibson, Rhodri Fabbro, Alma Rahat, Thomas Torsney-Weir, Daniel Archambault, Michael Gravenor, Biagio Lucini

Generative Design of Microfluidic Channel Geometry Using Evolutionary Approach Nikolay Nikitin, Alexander Hvatov, Iana Polonskaia, Anna Kalyuzhnaya, Georgii Grigorev, Xiaohao Wang, Xiang Qian Rapid Prototyping of Evolution-Driven Biclustering Methods in Julia Paweł Renc, Patryk Orzechowski, Jarosław W ˛as,Aleksander Byrski, Jason Moore

Winner Prediction for Real-time Strategy Games through Feature Selection Based on a Genetic Wrapper Seung-Soo Shin, Yong-Hyuk Kim k-Pareto Optimality for Many-Objective Genetic Optimization Jean Ruppert, Marharyta Aleksandrova, Thomas Engel

Women@GECCO

Organizers: Marie-Eléonore Kessaci (University of Lille); Swetha Varadarajan (Colorado State University)

Time: Monday, July 12, 15:45-18:00, Plenary Room

Welcome, Quiz Digital Technology and Gender Equality: A Challenge for Higher Education Isabelle Collet, University of Geneva, Switzerland Pursuing an interdisciplinary research career Melanie Mitchell, Santa Fe Institute, USA Reflections as Retirement Nears Valerie Barr, Mount Holyoke College, USA General Discussion

Humies, Competitions, Evolutionary Computation in Practice, Hot Off the Press, and Job Market 60 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

18th Annual Humies Awards for Human Competitive Results

Presentations: Monday, July 12, 14:20-15:40

Announcement of Awards: Wednesday, July 14, 15:40-17:10

On-location chair: Erik D. Goodman

Judging Panel: Erik D. Goodman, Una-May O’Reilly, Wolfgang Banzhaf, Darrell D. Whitley, Lee Spector, Stephanie Forrest

Publicity Chair: William Langdon

Prizes: prizes totaling $10,000 to be awarded

Detailed Information: www.human-competitive.org

Techniques of genetic and evolutionary computation are being increasingly applied to difficult real- world prob- lems— often yielding results that are not merely academically interesting, but competitive with the work done by creative and inventive humans. Starting at the Genetic and Evolutionary Computation Conference (GECCO) in 2004, cash prizes have been awarded for human competitive results that had been produced by some form of genetic and evolutionary computation in the previous year. The total prize money for the Humies awards is $10,000 US dollars. As a result of detailed consideration of the entries in this year’s Humies competition, the selected finalists will each be invited to give a short prerecorded video presentation during GECCO. Each presentation will be 10 minutes. The presentations are open to all GECCO participants. After the session the judges will confer and select winners for Bronze (either one prize of $2,000 or two prizes of $1,000) Silver ($3,000) and Gold ($5,000) awards. The awards will be announced during the GECCO closing ceremony. Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 61

Competitions

"Continuous" Interaction Testing

Organizers: Ryan Dougherty, Xi Jiang Time: Sunday, July 11, 11:00-15:20, Competitions Room

Interaction testing allows for determining a fault in a complex engineered system where a fault is the result of a "small" number of factors interacting. Typically, the object of study is a "covering array", which is an array of symbols (a given number of rows and columns) that encodes the tests to-be-performed: the rows are the tests, and the columns correspond to the factors/components of the system. The property of the array is that all interactions of size at most a given number (the "strength") appear in the array at least once. One main question to ask is: for a given system, when does a covering array exist? The most common goal in interaction testing is minimizing the number of rows/tests while maintaining the "coverage" property. It can be shown that as the number of factors increases, the minimum number of rows must increase. Showing a covering array existing is easy, but proving that one does not exist is very difficult, and often mathematicians result to case analyses to prove an array cannot exist. This competition is focused on a "continuous" generalization of covering arrays, which appear to allow (1) a more fine-grained approach to understand how "covering" an array is, and (2) possibly a better explanation for why certain arrays do not exist.

Bound Constrained Single Objective Numerical Optimization

Organizers: Ponnuthurai Nagaratnam Suganthan Time: Sunday, July 11, 11:00-15:20, Competitions Room

In this competition, participants will test their single objective numerical optimization algorithms on a selected 10D and 30D problems with / without a number of transformations such as shifting, rotation, shearing, etc.

Website: https://www3.ntu.edu.sg/home/epnsugan/index_files/cec-benchmarking.htm

Competition on Niching Methods for Multimodal Optimization

Organizers: Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan Fieldsend Time: Sunday, July 11, 11:00-15:20, Competitions Room

The aim of the competition is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The competition allows participants to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty. Researchers are welcome to evaluate their niching algorithms using this benchmark suite, and report the results by submitting a paper to the main tracks of GECCO (i.e., submitting via the online submission system of GECCO), or to hand in a GECCO short paper on their competition entry. The description of the benchmark suite, evaluation procedures, and established baselines can be found in the following technical report:

X. Li, A. Engelbrecht, and M.G. Epitropakis, “Benchmark Functions for CEC’2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization”, Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013.

Website: http://epitropakis.co.uk/gecco2021/ 62 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

Competition on the Optimal Camera Placement Problem (OCP) and the Unicost Set Covering Problem (USCP) Organizers: Mathieu Brévilliers, Julien Lepagnot, Lhassane Idoumghar Time: Sunday, July 11, 11:00-15:20, Competitions Room

The use of camera networks is now common to perform various surveillance tasks. These networks can be imple- mented together with intelligent systems that analyze video footage, for instance, to detect events of interest, or to identify and track objects or persons. According to the specialized literature, whatever the operational needs are, the quality of service depends on the way in which the cameras are deployed in the area to be monitored (in terms of position and orientation angles). Moreover, due to the prohibitive cost of setting or modifying such a camera network, it is required to provide a priori a configuration that minimizes the number of cameras in addition to meeting the operational needs. In this context, the optimal camera placement problem (OCP) is of critical importance, and can be generically formulated as follows. Given various constraints, usually related to coverage or image quality, and an objective to optimise (typically, the cost), how can the set of positions and orientations which best (optimally) meets the requirements be determined? More specifically, in this competition, the objective will be to determine camera locations and orientations which ensure complete coverage of the area while minimizing the cost of the infrastructure. To this aim, a discrete approach is considered here : the surveillance area is reduced to a set of three-dimensional sample points to be covered, and camera configurations are sampled into so-called candidates, each with a given set of position and orientation coordinates. A candidate can have several samples within range, and a sample can be seen by several candidates. Now, the OCP comes down to select the smallest subset of candidates which covers all the samples. The main goal of this competition is to encourage innovative research works in this direction, by proposing to solve OCP problem instances stated as USCP.

Website: http://www.mage.fst.uha.fr/brevilliers/gecco-2021-ocp-uscp-competition/

Dota 2 1-on-1 Shadow Fiend Laning Competition Organizers: Robert Smith, Malcolm Heywood, Alexandru Ianta Time: Sunday, July 11, 11:00-15:20, Competitions Room

The Dota 2 game represents an example of a multiplayer online battle arena video game. The underlying goal of the game is to control the behaviour/ strategy for a ‘hero’ character. A hero posses certain abilities, thus resulting in different performance tradeoffs. Moreover, the hero acts with a team of ‘creeps’ who have predefined behaviours, which can be influenced by the interaction between their hero and the opposing team. In short, the hero operates collaboratively with its own creeps and defensive structures (called towers) to defeat the opponent team (kill the opponent hero twice, or destroy their tower). In addition, there is an underlying economy in which developments in the game influence the amount of wealth received by each team. As a team’s wealth increases, then the hero’s abilities improve. This competition will assume the 1-on-1 mid lane configuration of Dota 2 using the Shadow Fiend hero. Such a configuration still includes many of the properties that have turned the game into an ‘e-sport’, but without the computational overhead of solving the task for all heroes under multi-lane settings. Specific properties that make the 1-on-1 game challenging include: 1) the need to navigate a partially observable world under ego-centric sensor information, 2) state information that is high-dimensional, but subject to variation through the ‘fog-of-war’, 3) high- dimensional action space that is both discrete, continuous valued and context specific, 4) learning hero policies that act collectively with creeps, 5) supporting real-time decision making at frame-rate, and 6) the underlying physics of the game vary with the times of day and introduce stochastic states. Participants will create a Dota 2 Shadow Fiend hero agent based on a preset API provided by the organizers. The competition entrants will be required to engage in a 1v1 match against the built-in Shadow Fiend hero AI, where the winner is determined by number of matches won. Evaluation will be performed against the top three levels of built-in hero over multiple games. Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 63

Website: https://web.cs.dal.ca/~dota2/

Dynamic Stacking Optimization in Uncertain Environments Organizers: Andreas Beham, Stefan Wagner, Sebastian Leitner, Johannes Karder, Bernhard Werth Time: Sunday, July 11, 11:00-15:20, Competitions Room

Stacking problems are central to multiple billion-dollar industries. The container shipping industry needs to stack millions of containers every year. In the steel industry the stacking of steel slabs, blooms, and coils needs to be carried out efficiently, affecting the quality of the final product. The immediate availability of data – thanks to the continuing digitalization of industrial production processes – makes the optimization of stacking problems in highly dynamic environments feasible. This competition extends the 2020 Dynamic Stacking competition with a second track. The first track is identical to the 2020 competition whereby a dynamic environment is provided that represents a simplified stacking scenario. Blocks arrive continuously at a fixed arrival location from which they have to be removed swiftly. If the arrival location is full, the arrival of additional blocks is not possible. To avoid such a state, there is a range of buffer stacks that may be used to store blocks. Each block has a due date before which it should be delivered to the customer. However, blocks may leave the system only when they become ready, i.e., some time after their arrival. To deliver a block it must be put on the handover stack – which must contain only a single block at any given time. There is a single crane that may move blocks from arrival to buffer, between buffers, and from buffer to handover. The optimization must control this crane in that it reacts to changes with a sequence of moves that are to be carried out. The control does not have all information about the world. A range of performance indicators will be used to determine the winner. The second track represents another stacking scenario that is derived from real-world scenarios. It features two cranes and two different handovers. The cranes have a capacity of larger than one which represents an additional challenge for the solver. Again the solver just provide the moves and the cranes will sort out the order in which these are performed using a simple heuristic. In this scenario, not the arrival stack is the critical part, but the handover stacks and thus the downstream process must not run empty. The dynamic environments are implemented in form of a realtime simulation which provides the necessary change events. The simulation runs in a separate process and publishes its world state and change events via message queuing (ZeroMQ), and also listens for crane orders. Thus, control algorithms may be implemented as standalone applications using a wide range of programming languages. Exchanged messages are encoded using protocol buffers – again libraries are available for a large range of programming languages.

Website: https://dynstack.adaptop.at

Evolutionary Computation in the Energy Domain: Smart Grid Applications Organizers: Fernando Lezama, Joao Soares, Bruno Canizes, Zita Vale, Ruben Romero Time: Sunday, July 11, 11:00-15:20, Competitions Room

Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competi- tion at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two testbeds in the energy domain:

• Testbed 1: Bi-level optimization of end-users’ bidding strategies in local energy markets (LM). This test bed is constructed under the same framework of the past competitions (therefore, former competitors can adapt their algorithms to this new testbed) , representing a complex bi-level problem in which competitive agents in the upper-level try to maximize their profits, modifying and depending on the price determined in the lower-level problem (i.e., the clearing price in the LM), thus resulting in a strong interdependence of their decisions.

• Testbed 2: Flexibility management of home appliances to support DSO requests. A model for aggregators flexibility provision in distribution networks that takes advantage of load flexibility resources allowing the 64 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) is proposed. The problem can be modeled as a Mixed-Integer Non-Linear Programming (MINLP) in which the aggregator strives to match a flexibility request from the DSO/BRP,paying a remuneration to the households participating in the DR program according to their preferences and the modification of their baseline profile.

The GECCO 2021 competition on “Evolutionary Computation in the Energy Domain: Smart Grid Applications” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely the optimal bidding of energy aggregators in local markets and the Flexibility management of home appliances to support DSO requests. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve two real-world optimization problems in the energy domain. The participants have the opportunity to evaluate if their algorithms can rank well in each independent problem since we understand the validity of the “no-free lunch theorem”, making this contest a unique opportunity worth to explore the applicability of the developed approaches in real-world problems beyond the typical benchmark and standardized CI problems.

Website: http://www.gecad.isep.ipp.pt/ERM-Competitions

Game Benchmark Competition Organizers: Vanessa Volz, Tea Tušar, Boris Naujoks Time: Sunday, July 11, 11:00-15:20, Competitions Room

The Game Benchmark for Evolutionary Algorithms (GBEA) is a collection of single- and multi-objective optimisation tasks that occur in applications to games research. We are proposing a competition with multiple tracks that addresses several different research questions featuring continuous and integer search spaces. The GBEA uses the COCO (COmparing Continuous Optimisers) framework for ease of integration. The task is to find solutions of sufficient quality (as specified by a target value) as quickly as possible. The competition is available in a single- and bi-objective version for two different applications, thus resulting in 4 different tracks. Details will be available on our website. Games are a very interesting topic that motivates a lot of research and have repeatedly been suggested as testbeds for AI algorithms. Key features of games are controllability, safety and repeatability, but also the ability to simulate properties of real-world problems such as measurement noise, uncertainty and the existence of multiple objectives. The motivation for the competition setup is as follows. If an algorithm for generating content (such as a Mario level or a Top Trumps deck) is integrated in a game, the goal is usually to provide replay value by varying the content. In this context, it is not necessary to find the single solution that optimises the designer’s objectives, but instead, it is important that a "good enough" solution can be found as fast as possible. "Good enough" in this case can be defined in relation to the values achieved by a baseline algorithm. Additionally, ideally, the same algorithm can find solutions across different objectives.

Website: http://www.gm.fh-koeln.de/~naujoks/gbea/

Minecraft Open-Endedness Competition Organizers: Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Sebastian Risi, and and Claire Glanois Time: Sunday, July 11, 11:00-15:20, Competitions Room

The purpose of this first contest on open-endedness is to highlight the progress in algorithms that can create novel and increasingly complex artefacts. While most experiments in open-ended evolution have so far focused on simple toy domains, we believe Minecraft - with its almost unlimited possibilities - is the perfect environment to study and compare such approaches. While other popular Minecraft competitions, like MineRL, have an agent-centric focus, in this competition the goal is to directly evolve Minecraft builds. Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 65

As part of this competition, we introduce the Minecraft Mechanical Creations Environment (MMCE) API. The MMCE is implemented as a mod for Minecraft that allows clients to manipulate blocks in a running Minecraft server programmatically through an API. The framework is specifically developed to facilitate experiments in artificial evolution. The competition framework also supports the recently added "redstone" circuit components in Minecraft, which allowed players to build amazing functional structures, such as bridge builders, battle robots, or even complete CPUs. Can an open-ended algorithm running in Minecraft discover similarly complex artefacts automatically? In contrast to the Minecraft Settlement Generation Challenge, this competition is more about - but not exclusively focused on - the evolution of mechanical/functional artefacts.

Website: https://evocraft.life/

Open Optimization Competition 2021: Competition and Benchmarking of Sampling-Based Optimization Algo- rithms Organizers: Carola Doerr, Olivier Teytaud, Jérémy Rapin, Thomas Bäck Time: Sunday, July 11, 11:00-15:20, Competitions Room

In order to promote research in black-box optimization, we organize a competition around Nevergrad and IOHpro- filer. The competition has two tracks:

• Track 1: Performance-Oriented Track: Contributors submit an optimization algorithm as a pull request in Nevergrad as detailed below. Several subtracks (“benchmark suites”) are available, covering a broad range of black-box optimization scenarios, from discrete over mixed-integer to continuous optimization, from “artificial” academic functions to real-world problems, from one-Shot Setting over sequential optimization to parallel settings.

• Track 2: General Benchmarking Practices: contributions are made by pull request to Nevergrad or IOHprofiler and are accompanied by a “paper style” documentation. This track invites contributions to all aspects of benchmarking black-box optimization algorithms, e.g., by suggesting new benchmark problems, performance measures, or statistics, by extending or improving the functionalities of the benchmarking environment, etc.

Website: http://www-ia.lip6.fr/~doerr/OpenOptimizationCompetition2021.html

Optimization of a Simulation Model for a Capacity and Resource Planning Task for Hospitals under Special Consideration of the COVID-19 Pandemic Organizers: Margarita Rebolledo, Frederik Rehbach, Sowmya Chandrasekaran, Thomas Bartz-Beielstein Time: Sunday, July 11, 11:00-15:20, Competitions Room

Similar to the many previous competitions, the team of the Institute of Data Science, Engineering, and Analytics at the TH Cologne (IDE+A), hosts the ’Industrial Challenge’ at the GECCO 2021. This year’s industrial challenge is posed in cooperation with an IDE+A partner from health industry and with Bartz & Bartz GmbH. Simulation models are valuable tools for resource usage estimation and capacity planning. Your goal is to determine improved simulation model parameters for a capacity and resource planning task for hospitals. The simulator, babsim.hospital, explicitly covers difficulties for hospitals caused by the COVID-19 pandemic. The simulator can handle many aspects of resource planning in hospitals:

• various resources such as ICU beds, ventilators, personal protection equipment, staff, pharmaceuticals

• several cohorts (based on age, health status, etc.).

The task represents an instance of an expensive, high-dimensional computer simulation-based optimization prob- lem and provides an easy evaluation interface that will be used for the setup of our challenge. The simulation 66 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

will be executed through an interface and hosted on one of our servers (similar to our last year’s challenge). The task is to find an optimal parameter configuration for the babsim.hospital simulator with a very limited budget of objective function evaluations. The best-found objective function value counts. There will be multiple versions of the babsim.hospital simulations, with slightly differing optimization goals, so that algorithms can be developed and tested before they are submitted for the final evaluation in the challenge. The participants will be free to apply one or multiple optimization algorithms of their choice. Thus, we enable each participant to apply his/her algorithms to a real problem from health industry, without software setup or licensing that would usually be required when working on such problems.

Website: https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-2021-industrial-challenge-call-for-participation_ 82086.php

Real-World Multi-Objective Optimization Competition Organizers: Ponnuthurai Suganthan Time: Sunday, July 11, 11:00-15:20, Competitions Room

In this competition, participants will solve a collection of real-world multi-objective optimization problems using their algorithms.

Website: https://www3.ntu.edu.sg/home/epnsugan/index_files/cec-benchmarking.htm

The AbstractSwarm Multi-Agent Logistics Competition Organizers: Daan Apeldoorn, Alexander Dockhorn, Lars Hadidi, Torsten Panholzer Time: Sunday, July 11, 11:00-15:20, Competitions Room

This competition aims to motivate work in the broad field of logistics. We have prepared a benchmarking framework which allows the development of multi-agent swarms to process a variety of test environments. Those can be ex- tremely diverse, highly dynamic and variable of size. The ultimate goal of this competition is to foster comparability of multi-agent systems in logistics-related problems (e. g., in hospital logistics). Many such problems have good accessibility and are easy to comprehend, but hard to solve. Problems of different difficulty have been designed to make the framework interesting for educational purposes. However, finding efficient solutions for different a priori unknown test environments remains a challenging task for practitioners and researchers alike. Following these ideas, in the AbstractSwarm Multi-Agent Logistics Competition, participants must develop agents that are able to cooperatively solve different a priori unknown logistics problems. A logistics problem is given as a graph containing agents and stations. An agent can interact with the graph (1) by deciding which station to visit next, (2) by communicating with other agents, and (3) by retrieving a reward for its previous decision. While simulating a scenario, a timetable in the form of a Gantt-chart is created according to the decisions of all agents. Submissions will be ranked according to the total number of idle time of all agents in several different a priori unknown problem scenarios in conjunction with the number of iterations needed to come to the solution.

Website: https://abstractswarm.gitlab.io/abstractswarm_competition/ Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 67

Competition posters Competition posters will be presented during the Poster Sessions, on Monday, July 12, 16:00-18:00 and Tuesday, July 13, 09:00-10:20, on Gather, together with Posters (p. 93) and Late-breaking Abstracts (p. 56).

An Evolutionary and Neighborhood-based Algorithm for Optimization under Low Budget Requirements Jordi Pereira Benchmarking Gradient-Free Optimizers for 3D Performance Capture in the Nevergrad platform Alexandros Doumanoglou, Nikolaos Zioulis, Vladimiros Sterzentsenko, Antonis Karakottas, Dimitrios Zarpalas, Petros Daras Cooperative Co-evolution Strategies with Time-dependent Grouping for Optimization Problems in Smart Grids Junpeng Su, Han Huang, Zhifeng Hao Exact and Approximate USCP With Branch and Bound Janez Radešˇcek, Matjaž Depolli Hospital Simulation Model Optimisation with a Random ReLU Expansion Surrogate Model Laurens Bliek, Arthur Guijt, Rickard Karlsson Linear Regression Strategy for Differential Evolution José Luis Sainz-Pardo Auñón Ring Cellular Encode-Decode UMDA: Simple is effective Ansel Y. Rodríguez-González, Samantha Barajas, Ramón Aranda, Yoan Martínez-López, Julio Madera-Quintana Robust Benchmarking for Multi-Objective Optimization Tome Eftimov, Peter Korošec SOMA-CLP for Competition on Bound Constrained Single Objective Numerical Optimization Benchmark Tomas Kadavy, Michal Pluhacek, Adam Viktorin, Roman Šenkeˇrík Surrogate-based Optimisation for a Hospital Simulation Scenario Using Pairwise Classifiers Pablo Naharro, Jose Peña, Antonio LaTorre 68 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

Competitions sessions

Competitions 1

Organizers: Marcella Scoczynski Ribeiro Martins (Federal University of Technology (UTFPR)); Markus Wagner (School of Computer Science, The University of Adelaide) Time: Sunday, July 11, 11:00-12:50, Room Competitions

Opening for Competitions Marcella Scoczynski, Markus Wagner Competition on "Continuous" Interaction Testing Ryan Dougherty, Xi Jiang Competition on Bound Constrained Single Objective Numerical Optimization Ponnuthurai Suganthan Real-World Multi-Objective Optimization Competition Ponnuthurai Suganthan Competition on the optimal camera placement problem (OCP) and the unicost set covering problem (USCP) Mathieu Brévilliers, Julien Lepagnot, Lhassane Idoumghar Competition on Dota 2 1-on-1 Shadow Fiend Laning Competition Robert Smith, Malcolm Heywood, Alexandru Ianta Competition on Dynamic Stacking Optimization in Uncertain Environments Andreas Beham, Stefan Wagner, Sebastian Leitner, Johannes Karder, Bernhard Werth

Competitions 2

Organizers: Marcella Scoczynski Ribeiro Martins (Federal University of Technology (UTFPR)); Markus Wagner (School of Computer Science, The University of Adelaide) Time: Sunday, July 11, 13:30-15:20, Room Competitions

Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications Fernando Lezama, Joao Soares, Bruno Canizes, Zita Vale, Ruben Romero Game Benchmark Competition Vanessa Volz, Tea Tušar, Boris Naujoks Minecraft Open-Endedness Competition Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Sebastian Risi Open Optimization Competition 2021: Competition and Benchmarking of Sampling-Based Optimization Algo- rithms Carola Doerr, Olivier Teytaud, Jérémy Rapin, Thomas Bäck Competition on Optimization of a simulation model for a capacity and resource planning task for hospitals under special consideration of the COVID-19 pandemic Margarita Rebolledo Coy, Frederik Rehbach, Sowmya Chandrasekaran, Thomas Bartz-Beielstein Competition on Niching Methods for Multimodal Optimization Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan Fieldsend Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 69

The AbstractSwarm Multi-Agent Logistics Competition Daan Apeldoorn, Alexander Dockhorn, Lars Hadidi, Torsten Panholzer 70 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

Evolutionary Computation in Practice

Organizers: Thomas Bartz-Beielstein, IDE+A, TH Köln Bogdan Filipiˇc, Jožef Stefan Institute Sowmya Chandrasekaran, IDE+A, TH Köln

In the Evolutionary Computation in Practice (ECiP) track, well-known speakers with outstanding reputation in academia and industry present background and insider information on how to establish reliable cooperation with industrial partners. They actually run companies or are involved in cooperations between academia and industry. If you attend, you will learn multiple ways to extend EC practice beyond the approaches found in textbooks. Experts in real-world optimization with decades of experience share their approaches to creating successful projects for real-world clients. Some of what they do is based on sound project management principles, and some is specific to our type of optimization projects. A panel of experts describes a range of techniques you can use to identify, design, manage, and successfully complete an EA project for a client. If you are working in academia and are interested in managing industrial projects, you will receive valuable hints for your own research projects.

Session 1: Bridging the gap between academia and industry Monday, July 12, 10:20-11:40 Chair: Bogdan Filipiˇc Room ECiP

Prescriptive Analytics in Production: Practical Applications of Evolutionary AI Michael Affenzeller, Andreas Beham Roadblocks to Finding Optimal Tunnel Alignments with Evolutionary Algorithms Tea Tušar Discussion

Session 2: ’Real’ real-world optimization Monday, July 12, 14:20-15:40 Chair: Sowmya Chandrasekaran Room ECiP

AI-Based Control of Conveyor Applications Using a Digital Twin for Model Training Niklas Körwer, Martin Bischoff Model-based Condition and Process Monitoring of Automated Assembly Systems Based on Machine Learning Methods Hans Fleischmann Discussion Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market 71

Hot off the Press

Organizer: Carola Doerr, CNRS and Sorbonne University

Time: – HOP1: Monday, July 12, 10:20-11:40, Room 7 – HOP2: Monday, July 12, 14:20-15:40, Room 7 – HOP3: Tuesday, July 13, 12:20-13:40, Room 7

The Hot Off the Press (HOP) track offers authors of recent papers the opportunity to present their work to the GECCO community, both by giving a talk on one of the three main days of the conference and by having a 2- page abstract appear in the Proceedings Companion, in which the workshop papers, late-breaking abstracts, and tutorials also appear. We invite researchers to submit summaries of their own work recently published in top-tier conferences and journals. Contributions are selected based on their scientific quality and their relevance to the GECCO community. Typical contributions include (but are not limited to) evolutionary computation papers that have appeared at venues different from GECCO, papers comparing different heuristics and optimization methods that have appeared at a general heuristics or optimization venue, papers describing applications of evolutionary methods that have appeared at venues of this application domain, or papers describing methods with relevance to the GECCO community that have appeared at a venue centred around this method’s domain. In any case, it is the authors’ responsibility to make clear why this work is relevant for the GECCO community, and to present the results in a language accessible to the GECCO community. 72 Humies, Competitions, Evolutionary Computation in Practice, Hot off the Press, and Job Market

Job Market

Organizers: Boris Naujoks, TH Köln - Cologne University of Applied Sciences Tea Tušar, Jožef Stefan Institute

Time: Monday, July 12, 13:40-14:20, Room Job Market

The GECCO Job Market is an event where people offering jobs can advertise open positions and meet with potential candidates. Any kind of positions are eligible (PhD, Postdoc, Professor, Engineer, etc.) - from the academia as well as the industry. The Job Market will be organized as a short virtual session (from 13:40 to 14:20) during a break on Monday, July 12, in Gather. After brief presentations of the available positions, participants will have the possibility to join face-to-face meetings for further discussions.

The collection of positions on offer can be found at the SIGEVO web site: https://sig.sigevo.org/index.html/tiki-index.php?page=Job+Ads+Listing SIGEVO Summer School 74 SIGEVO Summer School

SIGEVO Summer School (S3)

Organizers: Miguel Nicolau, University College Dublin Christine Zarges, Aberystwyth University Dates: July 5-9 Mentors: Jürgen Branke, University of Warwick Carola Doerr, CNRS and Sorbonne University Carlos Fonseca, University of Coimbra, Portugal Erik Hemberg, MIT Computer Science and Artificial Intelligence Lab Manuel López-Ibáñez, University of Manchester Leslie Pérez Cáceres, Pontificia Universidad Católica de Valparaíso

SIGEVO is a special interest group of ACM always looking for new ways to improve the research and learning on genetic and evolutionary computation. In this quest for disseminating the knowledge and making activities in this domain we organize a Summer School around GECCO 2021: S3 (SIGEVO Summer School). The SIGEVO Summer School (S3) goes into round 4 with a renewed format. We will be targeting the following areas of knowledge: General skills needed by GECCO researchers, e.g. reviewing, paper writing and presenting, as well as best practices for the empirical evaluation of evolutionary algorithms. Taught through lectures and small workshops led by summer school mentors. In-depth knowledge about specific topics within GECCO, mostly through attending tutorials and workshops associated with GECCO (included in the price). Some mentor sessions will also be tailored towards specific research topics to allow for more in-depth discussions with an expert in the field. Best Paper Nominations 76 Best Paper Nominations

Voting Instructions Beware: Each GECCO attendee has only one vote and can only vote for a single best paper session. The best paper session for which to vote can be decided after attending several best paper sessions (see below).

Procedure: Each track has nominated one or more papers for a best paper award (see the list below). There will be one award per “Best Paper” session. Papers competing for the same award are presented in the same “Best Paper” session. Nominees from small tracks are grouped together into the same session. The votes are nominative and cannot be delegated to another attendee. More detailed instructions of the procedure to vote during the online conference will be provided to all the attendees.

Best Paper Nominations

Ant Colony Optimization and Swarm Intelligence (ACO-SI)

A Rigorous Runtime Analysis of the 2-MMAS on Jump Functions: Ant Tuesday, July 13, 14:20-15:40 Colony Optimizer Can Cope Well With Local Optima Room ACO-SI Riade Benbaki, Ziyad Benomar, Benjamin Doerr

Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware) (CS)

Biodiversity in Evolved Voxel-based Soft Robots Monday, July 12, 10:20-11:40 Eric Medvet, Federico Pigozzi, Alberto Bartoli, Marco Rochelli Room CS Sparse Reward Exploration via Novelty Search and Emitters Monday, July 12, 10:20-11:40 Giuseppe Paolo, Alexandre Coninx, Stephane Doncieux, Alban Laflaquière Room CS

Evolutionary Combinatorial Optimization and Metaheuristics (ECOM)

A Graph Coloring based Parallel Hill Climber for Large-scale NK-landscapes Monday, July 12, 14:20-15:40 Bilel Derbel, Lorenzo Canonne Room ECOM Genetic Algorithm Niching by (Quasi-)Infinite Memory Monday, July 12, 14:20-15:40 Adrian Worring, Benjamin Mayer, Kay Hamacher Room ECOM

Evolutionary Machine Learning (EML)

Signal Propagation in a Gradient-Based and Evolutionary Learning System Tuesday, July 13, 12:20-13:40 Jamal Toutouh, Una-May O’Reilly Room EML

Evolutionary Multiobjective Optimization (EMO)

Greedy Approximated Hypervolume Subset Selection for Many-objective Tuesday, July 13, 14:20-15:40 Optimization Room EMO Ke Shang, Hisao Ishibuchi, Weiyu Chen Landscape Features and Automated Algorithm Selection for Multi-objective Tuesday, July 13, 14:20-15:40 Interpolated Continuous Optimisation Problems Room EMO Arnaud Liefooghe, Sébastien Verel, Benjamin Lacroix, Alexandru-Ciprian Z˘avoianu, John McCall Best Paper Nominations 77

Pareto Compliance from a Practical Point of View Tuesday, July 13, 14:20-15:40 Jesús Falcón-Cardona, Saúl Zapotecas-Martínez, Abel García-Nájera Room EMO

Evolutionary Numerical Optimization (ENUM)

A Matrix Adaptation Evolution Strategy for Optimization on General Tuesday, July 13, 14:20-15:40 Quadratic Manifolds Room ENUM Patrick Spettel, Hans-Georg Beyer

Genetic Algorithms (GA)

A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition- Tuesday, July 13, 12:20-13:40 based Ensemble Learning Room GA Arkadiy Dushatskiy, Tanja Alderliesten, Peter Bosman Analysis of Evolutionary Diversity Optimisation for Permutation Problems Tuesday, July 13, 12:20-13:40 Anh Do, Mingyu Guo, Aneta Neumann, Frank Neumann Room GA

General Evolutionary Computation and Hybrids (GECH)

Expressivity of Parameterized and Data-driven Representations in Quality Tuesday, July 13, 12:20-13:40 Diversity Search Room GECH Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck

Genetic Programming (GP)

A Generalizability Measure for Program Synthesis with Genetic Monday, July 12, 14:20-15:40 Programming Room GP Dominik Sobania, Franz Rothlauf PSB2: The Second Program Synthesis Benchmark Suite Monday, July 12, 14:20-15:40 Thomas Helmuth, Peter Kelly Room GP Towards Effective GP Multi-Class Classification Based on Dynamic Targets Monday, July 12, 14:20-15:40 Stefano Ruberto, Valerio Terragni, Jason Moore Room GP

Neuroevolution (NE)

Policy Gradient Assisted MAP-Elites Tuesday, July 13, 12:20-13:40 Olle Nilsson, Antoine Cully Room NE

Real World Applications (RWA)

Evolutionary Minimization of Traffic Congestion Monday, July 12, 10:20-11:40 Maximilian Böther, Leon Schiller, Philipp Fischbeck, Louise Molitor, Martin Room RWA Krejca, Tobias Friedrich Level Generation for Angry Birds with Sequential VAE and Latent Variable Monday, July 12, 10:20-11:40 Evolution Room RWA Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto 78 Best Paper Nominations

Theory (Theory)

Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Tuesday, July 13, 14:20-15:40 Why Success Rates Matter Room THEORY Mario Hevia Fajardo, Dirk Sudholt Papers and Posters 80 Paper Sessions, Monday, July 12, 10:20-11:40

NE 1 Monday, July 12, 10:20-11:40, Room NE Chair: Bing Xue (Victoria University of Wellington)

Using Novelty Search to Explicitly Create Diversity in Ensembles of Classifiers 10:20 Rui Cardoso, Emma Hart, David Kurka, Jeremy Pitt Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution 10:40 Nemanja Rakicevic, Antoine Cully, Petar Kormushev Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the 11:00 Number of Rules Joachim Pedersen, Sebastian Risi Genetic Crossover in the Evolution of Time-dependent Neural Networks 11:20 Jason Orlosky, Tim Grabowski

GECH 1 Monday, July 12, 10:20-11:40, Room GECH Chair: Carola Doerr (CNRS, Sorbonne University)

Parallel Differential Evolution Applied to Interleaving Generation with Precedence Evaluation of 10:20 Tentative Solutions Hayato Noguchi, Tomohiro Harada, Ruck Thawonmas

Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax 10:40 Maxim Buzdalov, Carola Doerr Personalizing Performance Regression Models to Black-Box Optimization Problems 11:00 Tome Eftimov, Anja Jankovic, Gorjan Popovski, Carola Doerr, Peter Korošec Adaptive Scenario Subset Selection for Minimax Black-Box Continuous Optimization 11:20 Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

HOP 1 Monday, July 12, 10:20-11:40, Room HOP Chair: Vladimir Mironovich (ITMO University)

On Sampling Error in Evolutionary Algorithms 10:20 Dirk Schweim, David Wittenberg, Franz Rothlauf Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property (Hot-off-the- 10:30 Press Track at GECCO 2021) Weijie Zheng, Huanhuan Chen, Xin Yao Realistic Constrained Multiobjective Optimization Benchmark Problems from Design 10:40 Cyril Picard, Jürg Schiffmann Achieving Weight Coverage for an Autonomous Driving System with Search-based Test 10:50 Generation (HOP Track at GECCO 2021) Thomas Laurent, Paolo Arcaini, Fuyuki Ishikawa, Anthony Ventresque Interactive Parameter Tuning of Bi-objective Optimisation Algorithms Using the Empirical 11:00 Attainment Function Manuel López-Ibáñez, Juan Diaz Paper Sessions, Monday, July 12, 14:20-15:40 81

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based 11:10 Allocation of Reproductive Trials Is Best Dogan Corus, Pietro Oliveto, Andrei Lissovoi, Carsten Witt

Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms 11:20 Malte Probst, Franz Rothlauf

RWA 1 - BP Monday, July 12, 10:20-11:40, Room RWA Chair: Richard Allmendinger (University of Manchester)

Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution 10:20 Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

Evolutionary Minimization of Traffic Congestion 10:40 Maximilian Böther, Leon Schiller, Philipp Fischbeck, Louise Molitor, Martin Krejca, Tobias Friedrich

CS 1 - BP Monday, July 12, 10:20-11:40, Room CS Chair: Dennis Wilson (ISAE-SUPAERO, University of Toulouse)

Sparse Reward Exploration via Novelty Search and Emitters 10:20 Giuseppe Paolo, Alexandre Coninx, Stephane Doncieux, Alban Laflaquière

Biodiversity in Evolved Voxel-based Soft Robots 10:40 Eric Medvet, Federico Pigozzi, Alberto Bartoli, Marco Rochelli

The Environment and Body-Brain Complexity 11:00 Geoff Nitschke, Christina Spanellis, Brooke Stewart

ECOM 1 Monday, July 12, 10:20-11:40, Room ECOM Chair: Sara Tari (LERIA)

Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem 10:20 Jakob Bossek, Frank Neumann

A Two-Stage Multi-Objective Genetic Programming with Archive for Uncertain Capacitated Arc 10:40 Routing Problem Shaolin Wang, Yi Mei, Mengjie Zhang

Real-like MAX-SAT Instances and the Landscape Structure Across the Phase Transition 11:00 Francisco Chicano, Gabriela Ochoa, Marco Tomassini

Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained 11:20 Monotone Submodular Functions Aneta Neumann, Jakob Bossek, Frank Neumann 82 Paper Sessions, Monday, July 12, 14:20-15:40

GA 1 Monday, July 12, 14:20-15:40, Room GA Chair: Darrell Whitley (Colorado State University)

Simulated Annealing for Symbolic Regression 14:20 Daniel Kantor, Fernando Von Zuben, Fabricio de Franca Partition Crossover for Continuous Optimization: ePX 14:40 Renato Tinós, Darrell Whitley, Francisco Chicano, Gabriela Ochoa

Quadratization of Gray Coded Representations, Long Path Problems and Needle Functions 15:00 Darrell Whitley, Francisco Chicano, Hernan Aquirre

A Parallel Ensemble Genetic Algorithm for the Traveling Salesman Problem 15:20 Swetha Varadarajan, Darrell Whitley

HOP 2 Monday, July 12, 14:20-15:40, Room HOP Chair: Johann Dreo (Thales Research & Technology)

The Influence of Uncertainties on Optimization of Vaccinations on a Network of Animal 14:20 Movements Krzysztof Michalak, Mario Giacobini

Do Quality Indicators Prefer Particular Multi-Objective Search Algorithms in Search-Based 14:30 Software Engineering? (Hot Off the Press track at GECCO 2021) Shaukat Ali, Paolo Arcaini, Tao Yue Genetic Improvement of Data for Maths Functions 14:40 William Langdon, Oliver Krauss

An evolutionary many-objective approach to multiview clustering using feature and relational 14:50 data Adán José-García, Julia Handl, Wilfrido Gómez-Flores, Mario Garza-Fabre Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation 15:00 George De Ath, Richard Everson, Alma Rahat, Jonathan Fieldsend

Multi-Objective Parameter-less Population Pyramid in Solving the Real-World and Theoretical 15:10 Problems Michal Przewozniczek, Piotr Dziurzanski, Shuai Zhao, Leandro Indrusiak Optimal Recombination and Adaptive Restarts Improve GA Performance on the Asymmetric TSP 15:20 Anton Eremeev, Yulia Kovalenko

GECH 2 Monday, July 12, 14:20-15:40, Room GECH Chair: Dirk Thierens (Utrecht University)

A hybrid CP/MOLS Approach for Multi-Objective Imbalanced Classification 14:20 Nicolas Szczepanski, Gilles Audemard, Laetitia Jourdan, Christophe Lecoutre, Lucien Mousin, Nadarajen Veerapen

Coevolutionary Modeling of Cyber Attack Patterns and Mitigations Using Public Datasets 14:40 Michal Shlapentokh-Rothman, Jonathan Kelly, Avital Baral, Erik Hemberg, Una-May O’Reilly Paper Sessions, Monday, July 12, 14:20-15:40 83

Directing Evolution: The Automated Design of Evolutionary Pathways Using Directed Graphs 15:00 Braden Tisdale, Deacon Seals, Aaron Pope, Daniel Tauritz PSAF: A Probabilistic Surrogate-Assisted Framework for Single-Objective Optimization 15:20 Julian Blank, Kalyanmoy Deb

ACO-SI 1 Monday, July 12, 14:20-15:40, Room ACO-SI Chair: Christopher Cleghorn (University of the Witwatersrand)

A hybrid ant colony optimization algorithm for the knapsack problem with a single continuous 14:20 variable Xinhua Yang, Yufan Zhou, Ailing Shen, Juan Lin, Yiwen Zhong Fishing for Interactions: A Network Science Approach to Modeling Fish School Search 14:40 Mariana Macedo, Lydia Taw, Nishant Gurrapadi, Rodrigo Lira, Diego Pinheiro, Marcos Oliveira, Carmelo Bastos-Filho, Ronaldo Menezes Ants can solve the parallel drone scheduling traveling salesman problem 15:00 Quoc Trung Dinh, Duc Dong Do, Minh Hoàng Hà Stasis type particle stability in a stochastic model of particle swarm optimization 15:20 Tomasz Kulpa, Krzysztof Trojanowski, Krzysztof Wójcik

GP 1 - BP Monday, July 12, 14:20-15:40, Room GP Chair: Mengjie Zhang (Victoria University of Wellington)

Towards Effective GP Multi-Class Classification Based on Dynamic Targets 14:20 Stefano Ruberto, Valerio Terragni, Jason Moore

PSB2: The Second Program Synthesis Benchmark Suite 14:40 Thomas Helmuth, Peter Kelly

A Generalizability Measure for Program Synthesis with Genetic Programming 15:00 Dominik Sobania, Franz Rothlauf

ECOM 2 - BP Monday, July 12, 14:20-15:40, Room ECOM Chair: Gabriela Ochoa (University of Stirling)

A Graph Coloring based Parallel Hill Climber for Large-scale NK-landscapes 14:20 Bilel Derbel, Lorenzo Canonne

Genetic Algorithm Niching by (Quasi-)Infinite Memory 14:40 Adrian Worring, Benjamin Mayer, Kay Hamacher Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems 15:00 Ekhiñe Irurozki, Manuel López-Ibáñez On the Design and Anytime Performance of Indicator-based Branch and Bound for Multi- 15:20 objective Combinatorial Optimization Alexandre Jesus, Luís Paquete, Bilel Derbel, Arnaud Liefooghe 84 Paper Sessions, Tuesday, July 13, 12:20-13:40

EMO 1 Tuesday, July 13, 12:20-13:40, Room EMO Chair: Ed Keedwell (University of Exeter)

Environmental Selection Using a Fuzzy Classifier for Multiobjective Evolutionary Algorithms 12:20 Jinyuan Zhang, Hisao Ishibuchi, Ke Shang, Linjun He, Lie Meng Pang, Yiming Peng

Quick Extreme Hypervolume Contribution Algorithm 12:40 Andrzej Jaszkiewicz, Piotr Zielniewicz

Distance-based Subset Selection Revisited 13:00 Ke Shang, Hisao Ishibuchi, Yang Nan

Bayesian Preference Learning for Interactive Multi-objective Optimisation 13:20 Kendall Taylor, Huong Ha, Minyi Li, Jeffrey Chan, Xiaodong Li

HOP 3 Tuesday, July 13, 12:20-13:40, Room HOP Chair: Katherine M. Malan (University of South Africa)

Reducing Bias in Multi-Objective Optimization Benchmarking 12:20 Tome Eftimov, Peter Korošec

Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives (Hot- 12:30 off-the-Press Track at GECCO 2021) Benjamin Doerr, Weijie Zheng

Genetic Improvement of Routing in Delay Tolerant Networks 12:40 Michela Lorandi, Leonardo Custode, Giovanni Iacca

Runtime Analysis via Symmetry Arguments (Hot-off-the-Press Track at GECCO 2021) 12:50 Benjamin Doerr

Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle 13:00 Routing Problems (Hot-off-the-Press Track at GECCO 2021) Hao Jiang, Yuhang Wang, Ye Tian, Xingyi Zhang, Jianhua Xiao

Improving Assertion Oracles with Evolutionary Computation 13:10 Valerio Terragni, Gunel Jahangirova, Mauro Pezzè, Paolo Tonella

On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints 13:20 Alma Rahat, Michael Wood

ACO-SI 2 Tuesday, July 13, 12:20-13:40, Room ACO-SI Chair: Mardé Helbig (Griffith University)

The Paradox of Choice in Evolving Swarms: Information Overload Leads to Limited Sensing 12:20 Calum Imrie, Michael Herrmann, Olaf Witkowski

A Bio-Inspired Spatial Defence Strategy for Collective Decision Making in Self-Organized Swarms 12:40 Judhi Prasetyo, Giulia De Masi, Raina Zakir, Muhanad Alkilabi, Elio Tuci, Eliseo Ferrante Paper Sessions, Tuesday, July 13, 14:20-15:40 85

RWA 2 Tuesday, July 13, 12:20-13:40, Room RWA Chair: Doron Haritan (Nvidia)

A simple evolutionary algorithm guided by local mutations for an efficient RNA design. 12:20 Cyrille Merleau Nono Saha, Matteo Smerlak Zeroth-Order Optimizer Benchmarking for 3D Performance Capture 12:40 Alexandros Doumanoglou, Petros Drakoulis, Kyriaki Christaki, Nikolaos Zioulis, Vladimiros Sterzentsenko, Antonis Karakottas, Dimitrios Zarpalas, Petros Daras Accelerated Evolutionary Induction of Heterogeneous Decision Trees for Gene Expression-Based 13:00 Classification Marcin Czajkowski, Krzysztof Jurczuk, Marek Kretowski Design of Specific Primer Sets for SARS-CoV-2 Variants Using Evolutionary Algorithms 13:20 Alejandro Lopez Rincon, Carmina Perez Romero, Lucero Mendoza-Maldonado, Eric Claassen, Johan Garssen, Aletta Kraneveld, Alberto Tonda

GA 2 - BP Tuesday, July 13, 12:20-13:40, Room GA Chair: Renato Tinós (University of São Paulo)

Analysis of Evolutionary Diversity Optimisation for Permutation Problems 12:20 Anh Do, Mingyu Guo, Aneta Neumann, Frank Neumann A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble 12:40 Learning Arkadiy Dushatskiy, Tanja Alderliesten, Peter Bosman Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem 13:00 Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary 13:20 Algorithms Jakob Bossek, Aneta Neumann, Frank Neumann

EML 1 + GECH 3 + NE 2 - BP Tuesday, July 13, 12:20-13:40, Rooms EML, NE, GECH Chair: Jaume Bacardit (Newcastle University)

Policy Gradient Assisted MAP-Elites 12:20 Olle Nilsson, Antoine Cully

Signal Propagation in a Gradient-Based and Evolutionary Learning System 12:40 Jamal Toutouh, Una-May O’Reilly

Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search 13:00 Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck Genetic Adversarial Training of Decision Trees 13:20 Francesco Ranzato, Marco Zanella 86 Paper Sessions, Tuesday, July 13, 14:20-15:40

NE 3 Tuesday, July 13, 14:20-15:40, Room NE Chair: Gisele Lobo Pappa (Federal University of Minas Gerais - UFMG)

A Geometric Encoding for Neural Network Evolution 14:20 Paul Templier, Emmanuel Rachelson, Dennis Wilson

Evolving Neural Architecture Using One Shot Model 14:40 Nilotpal Sinha, Kuan-Wen Chen

Training Spiking Neural Networks with a Multi-Agent Evolutionary Robotics Framework 15:00 Souvik Das, Anirudh Shankar, Vaneet Aggarwal

Fitness Landscape Analysis of Graph Neural Network Architecture Search Spaces 15:20 Matheus Nunes, Paulo Fraga, Gisele Pappa

GP 2 Tuesday, July 13, 14:20-15:40, Room GP Chair: Stefano Cagnoni (University of Parma)

CoInGP: Convolutional Inpainting with Genetic Programming 14:20 Domagoj Jakobovic, Luca Manzoni, Luca Mariot, Stjepan Picek, Mauro Castelli

Evolvability and Complexity Properties of the Digital Circuit Genotype-Phenotype Map 14:40 Alden Wright, Cheyenne Laue

Zoetrope Genetic Programming for regression 15:00 Aurelie Boisbunon, Carlo Fanara, Ingrid Grenet, Jonathan Daeden, Alexis Vighi, Marc Schoenauer

Measuring Feature Importance of Symbolic Regression Models Using Partial Effects 15:20 Guilherme Aldeia, Fabricio de Franca

EML 2 Tuesday, July 13, 14:20-15:40, Room EML Chair: Christian Gagné (Université Laval)

Optimizing Loss Functions Through Multi-Variate Taylor Polynomial Parameterization 14:20 Santiago Gonzalez, Risto Miikkulainen

An Effective Action Covering for Multi-label Learning Classifier Systems: A Graph-Theoretic 14:40 Approach Shabnam Nazmi, Abdollah Homaifar, Mohd Anwar

A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for 15:00 Molecular Property Prediction Yingfang Yuan, Wenjun Wang, Wei Pang

Regularized Evolutionary Population-Based Training 15:20 Jason Liang, Santiago Gonzalez, Hormoz Shahrzad, Risto Miikkulainen Paper Sessions, Tuesday, July 13, 14:20-15:40 87

ECOM 3 Tuesday, July 13, 14:20-15:40, Room ECOM Chair: Luís Paquete (University of Coimbra)

Generating Hard Inventory Routing Problem Instances using Evolutionary Algorithms 14:20 Krzysztof Michalak

Local Search Pivoting Rules and the Landscape Global Structure 14:40 Sara Tari, Gabriela Ochoa An Efficient Implementation of Iterative Partial Transcription for the Traveling Salesman 15:00 Problem Anirban Mukhopadhyay, Darrell Whitley, Renato Tinós

The tiebreaking space of constructive heuristics for the permutation flowshop minimizing 15:20 makespan Marcus Ritt, Alexander Benavides

CS 2 Tuesday, July 13, 14:20-15:40, Room CS Chair: Georgios N. Yannakakis (Institute of Digital Games, University of Malta, Malta; University of Malta)

On the Impact of Tangled Program Graph Marking Schemes under the Atari Reinforcement 14:20 Learning Benchmark Alexandru Ianta, Ryan Amaral, Caleidgh Bayer, Robert Smith, Malcolm Heywood

MAEDyS: Multiagent Evolution via Dynamic Skill Selection 14:40 Enna Sachdeva, Shauharda Khadka, Somdeb Majumdar, Kagan Tumer

Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man 15:00 Benjamin Capps, Jacob Schrum

Resource Availability and The Evolution of Cooperation in a 3D Agent-Based Simulation 15:20 Lara Dal Molin, Jasmeen Kanwal, Christopher Stone

ACO-SI 3 + ENUM 1 + THEORY 1 - BP Tuesday, July 13, 14:20-15:40, Rooms THEORY, ENUM, ACO-SI Chair: Andrew M. Sutton (University of Minnesota Duluth)

A Rigorous Runtime Analysis of the 2-MMAS on Jump Functions: Ant Colony Optimizer Can Cope 14:20 Well With Local Optima Riade Benbaki, Ziyad Benomar, Benjamin Doerr

Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates 14:40 Matter Mario Hevia Fajardo, Dirk Sudholt

A Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds 15:00 Patrick Spettel, Hans-Georg Beyer

Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law 15:20 Distribution Denis Antipov, Maxim Buzdalov, Benjamin Doerr 88 Paper Sessions, Wednesday, July 14, 10:20-11:40

RWA 3 Tuesday, July 13, 14:20-15:40, Room RWA Chair: Igor Vatolkin (TU Dortmund)

A Genetic Algorithm Approach to Virtual Topology Design for Multi-Layer Communication 14:20 Networks Uwe Bauknecht Multi-Objective Optimization of Item Selection in Computerized Adaptive Testing 14:40 Dena Mujtaba, Nihar Mahapatra

MA-ABC: A Memetic Algorithm Optimizing Attractiveness, Balance, and Cost for Capacitated Arc 15:00 Routing Problems Muhilan Ramamoorthy, Violet Syrotiuk, Stephanie Forrest

Evaluating Medical Aesthetics Treatments through Evolved Age-Estimation Models 15:20 Risto Miikkulainen, Elliot Meyerson, Xin Qiu, Ujjayant Sinha, Raghav Kumar, Karen Hofmann, Yiyang Yan, Michael Ye, Jingyuan Yang, Damon Caiazza, Stephanie Manson Brown

EMO 2 - BP Tuesday, July 13, 14:20-15:40, Room EMO Chair: Laetitia Jourdan (University of Lille; University of Lille, Central Lille)

Landscape Features and Automated Algorithm Selection for Multi-objective Interpolated 14:20 Continuous Optimisation Problems Arnaud Liefooghe, Sébastien Verel, Benjamin Lacroix, Alexandru-Ciprian Z˘avoianu, John McCall

Pareto Compliance from a Practical Point of View 14:40 Jesús Falcón-Cardona, Saúl Zapotecas-Martínez, Abel García-Nájera

Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization 15:00 Ke Shang, Hisao Ishibuchi, Weiyu Chen

GP 3 Wednesday, July 14, 10:20-11:40, Room GP Chair: Penousal Machado (University of Coimbra)

Genetic Programming is Naturally Suited to Evolve Bagging Ensembles 10:20 Marco Virgolin

Speed Benchmarking of Genetic Programming Frameworks 10:40 Francisco Baeta, João Correia, Tiago Martins, Penousal Machado

Cooperative Coevolutionary Multiobjective Genetic Programming for Microarray Data 11:00 Classification Yang Qing, Chi Ma, Yu Zhou, Xiao Zhang, Haowen Xia

A Novel Multi-Task Genetic Programming Approach to Uncertain Capacitated Arc Routing 11:20 Problem Mazhar Ansari Ardeh, Yi Mei, Mengjie Zhang Paper Sessions, Wednesday, July 14, 10:20-11:40 89

CS 3 Wednesday, July 14, 10:20-11:40, Room CS Chair: Amy K. Hoover (New Jersey Institute of Technology)

Multi-Emitter MAP-Elites: Improving quality, diversity and data efficiency with heterogeneous 10:20 sets of emitters Antoine Cully

Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem 10:40 Konstantinos Sfikas, Antonios Liapis, Georgios Yannakakis

BR-NS: an Archive-less Approach to Novelty Search 11:00 Achkan Salehi, Alexandre Coninx, Stephane Doncieux

Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms 11:20 Leo Cazenille

EML 3 Wednesday, July 14, 10:20-11:40, Room EML Chair: Jamal Toutouh (Massachusetts Inst. of Technology, University of Málaga)

A survey of cluster validity indices for automatic data clustering using differential evolution 10:20 Adán José-García, Wilfrido Gómez-Flores

Genetic Programming for Borderline Instance Detection in High-dimensional Unbalanced 10:40 Classification Wenbin Pei, Bing Xue, Lin Shang, Mengjie Zhang

Convergence Analysis of Rule-Generality on the XCS Classifier System 11:00 Yoshiki Nakamura, Motoki Horiuchi, Masaya Nakata

Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive 11:20 Maintenance Tanja Tornede, Alexander Tornede, Marcel Wever, Eyke Hüllermeier

SBSE 1 Wednesday, July 14, 10:20-11:40, Room SBSE Chair: Fuyuki Ishikawa (National Institute of Informatics); Inmaculada Medina-Bulo (Universidad de Cádiz)

Encoding the Certainty of Boolean Variables to Improve the Guidance for Search-Based Test 10:20 Generation Sebastian Vogl, Sebastian Schweikl, Gordon Fraser

Concurrent Model Synchronisation with Multiple Objectives 10:40 Nils Weidmann, Gregor Engels

Analyzing the Impact of Product Configuration Variations on Advanced Driver Assistance Systems 11:00 with Search Kaiou Yin, Paolo Arcaini, Tao Yue, Shaukat Ali 90 Paper Sessions, Wednesday, July 14, 10:20-11:40

ENUM 2 Wednesday, July 14, 10:20-11:40, Room ENUM Chair: Petr Pošík (Czech Technical University in Prague)

Interaction between Model and Its Evolution Control in Surrogate-Assisted CMA Evolution 10:20 Strategy ZbynˇekPitra, Marek Hanuš, Jan Koza, JiˇríTumpach, Martin Holeˇna

Explorative Data Analysis of Time Series based Algorithm Features of CMA-ES Variants 10:40 Jacob de Nobel, Hao Wang, Thomas Baeck

Saddle Point Optimization with Approximate Minimization Oracle 11:00 Youhei Akimoto Augmented Lagrangian, penalty techniques and surrogate modeling for constrained optimization 11:20 with CMA-ES Paul Dufossé, Nikolaus Hansen

RWA 4 Wednesday, July 14, 10:20-11:40, Room RWA Chair: Anna Kononova (Leiden University)

An Evolutionary Multi-Objective Feature Selection Approach for Detecting Music Segment 10:20 Boundaries of Specific Types Igor Vatolkin, Fabian Ostermann, Meinard Müller

Evolutionary Meta Reinforcement Learning for Portfolio Optimization 10:40 Myoung Hoon Ha, Seung-geun Chi, Sangyeop Lee, Yujin Cha, Byung-Ro Moon

Continuously Running Genetic Algorithm for Real-Time Networking Device Optimization 11:00 Amit Mandelbaum, Doron Haritan, Natali Shechtman

Multi-objective Optimization Across Multiple Concepts: A Case Study on Lattice Structure Design 11:20 Brandon Parker, Hemant Singh, Tapabrata Ray

THEORY 2 Wednesday, July 14, 10:20-11:40, Room THEORY Chair: Frank Neumann (The University of Adelaide)

More Precise Runtime Analyses of Non-elitist EAs in Uncertain Environments 10:20 Per Kristian Lehre, Xiaoyu Qin

Stagnation Detection in Highly Multimodal Fitness Landscapes 10:40 Amirhossein Rajabi, Carsten Witt

Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with 11:00 Correlated Uniform Weights Yue Xie, Aneta Neumann, Frank Neumann, Andrew Sutton

Lower Bounds from Fitness Levels Made Easy 11:20 Benjamin Doerr, Timo Kötzing Paper Sessions, Wednesday, July 14, 12:20-13:40 91

EMO 3 Wednesday, July 14, 10:20-11:40, Room EMO Chair: Manuel López-Ibáñez (University of Málaga, University of Manchester)

Hypervolume in Biobjective Optimization Cannot Converge Faster Than Ω(1/p) 10:20 Eugénie Marescaux, Nikolaus Hansen

Interactive evolutionary multiple objective optimization algorithm using a fast calculation of 10:40 holistic acceptabilities Michał Tomczyk, Miłosz Kadzi´nski

Metric for Evaluating Normalization Methods in Multiobjective Optimization 11:00 Linjun He, Hisao Ishibuchi, Dipti Srinivasan

Realistic Utility Functions Prove Difficult for State-of-the-Art Interactive Multiobjective 11:20 Optimization Algorithms Seyed Shavarani, Manuel López-Ibáñez, Joshua Knowles

ENUM 3 Wednesday, July 14, 12:20-13:40, Room ENUM Chair: Martin Holena (Institute of Computer Science)

Self-Referential Quality Diversity Through Differential Map-Elites 12:20 Tae Jong Choi, Julian Togelius

Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality 12:40 Reduction Framework Ryoji Tanabe

THEORY 3 Wednesday, July 14, 12:20-13:40, Room THEORY Chair: Andrew M. Sutton (University of Minnesota Duluth)

Non-elitist Evolutionary Algorithms excel in Fitness Landscapes with Sparse Deceptive Regions 12:20 and Dense Valleys Duc-Cuong Dang, Anton Eremeev, Per Kristian Lehre

Generalized Jump Functions 12:40 Henry Bambury, Antoine Bultel, Benjamin Doerr

Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on 13:00 Convex Quadratic Function Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

RWA 5 Wednesday, July 14, 12:20-13:40, Room RWA Chair: Aneta Neumann (The University of Adelaide)

An Efficient Computational Approach for Automatic Itinerary Planning on Web Servers 12:20 Zeyuan Ma, Hongshu Guo, Yinxuan Gui, Yue-Jiao Gong 92 Paper Sessions, Wednesday, July 14, 12:20-13:40

A Genetic Algorithm For AC Optimal Transmission Switching 12:40 Masood Jabarnejad Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance 13:00 Constraints Yue Xie, Aneta Neumann, Frank Neumann Solving the Paintshop Scheduling Problem with Memetic Algorithms 13:20 Wolfgang Weintritt, Nysret Musliu, Felix Winter

GECH 4 + IMPACT Wednesday, July 14, 12:20-13:40, Room GECH Chair: Manuel López-Ibáñez (University of Málaga, University of Manchester)

The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and 12:20 Algorithm Selection Anja Jankovic, Tome Eftimov, Gorjan Popovski, Carola Doerr When Non-Elitism Meets Time-Linkage Problems 12:40 Weijie Zheng, Qiaozhi Zhang, Huanhuan Chen, Xin Yao (Looking Back on) Evolving a Diversity of Creatures through Novelty Search and Local 13:00 Competition Kenneth Stanley, Joel Lehman (A Brief Look at) The Evolution of Exploratory Landscape Analysis 13:20 Pascal Kerschke, Mike Preuss

CS 4 Wednesday, July 14, 12:20-13:40, Room CS Chair: Stéphane Doncieux (Sorbonne Universite; CNRS, ISIR)

Evolving Soft Robotic Jamming Grippers 12:20 Seth Fitzgerald, Gary Delaney, David Howard, Frederic Maire Evolving Gaits for Damage Control in a Hexapod Robot 12:40 Geoff Nitschke, Christopher Mailer, Leanne Raw A Signal-Centric Perspective on the Evolution of Symbolic Communication 13:00 Quintino Lotito, Leonardo Custode, Giovanni Iacca Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity 13:20 Modular Robots Enrico Zardini, Davide Zappetti, Davide Zambrano, Giovanni Iacca, Dario Floreano

GA 3 Wednesday, July 14, 12:20-13:40, Room GA Chair: Carlos Segura (Centro de Investigación en Matemáticas (CIMAT))

A Genetic Algorithm Approach for the Euclidean Steiner Tree Problem with Soft Obstacles 12:20 Manou Rosenberg, Mark Reynolds, Tim French, Lyndon While Direct linkage discovery with empirical linkage learning 12:40 Michal Przewozniczek, Marcin Komarnicki, Bartosz Frej Evolutionary Algorithms-assisted Construction of Cryptographic Boolean Functions 13:00 Claude Carlet, Domagoj Jakobovic, Stjepan Picek Poster Session 93

Please notice that all posters will be presented twice, in two poster sessions: on Monday evening and Tuesday morning (CEST time). The list of posters for both poster sessions is provided below. Late-breaking Abstracts (p. 56) and Competition Entries (p. 67) will be also presented as posters within both these sessions. Poster Sessions From Monday, July 12, 16:00 to Tuesday, July 13, 10:20, Poster Room 1 & 2 (Gather)

"Re-ID BUFF": An Enhanced Similarity Measurement Based on Genetic Programming for Person Re- identification Yiming Li, Lin Shang

A Benchmark Generator of Tree Decomposition Mk Landscapes Dirk Thierens, Tobias Driessel A Coevolutionary Approach to Deep Multi-Agent Reinforcement Learning Daan Klijn, Guszti Eiben

A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems Urban Škvorc, Tome Eftimov, Peter Korošec A Crossover That Matches Diverse Parents Together in Evolutionary Algorithms Maciej Swiechowski´

A Differential Evolution Reference Point Method for Set-based Robustness by Means of the Averaged Hausdorff Distance Carlos Hernandez Castellanos A Empirical Study of Cooperative Frequency in Cooperative Co-evolution Ling-Yu Li, Wen-Jie Ou, Xiao-Min Hu, Wei-Neng Chen, An Song

A Genetic Algorithm Approach to Compute Mixed Strategy Solutions for General Stackelberg Games Srivathsa Gottipati, Praveen Paruchuri

A Grouping Genetic Algorithm for the Unrelated Parallel-Machine Scheduling Problem Octavio Ramos-Figueroa, Marcela Quiroz-Castellanos

A Hybrid Local Search Framework for the Dynamic Capacitated Arc Routing Problem Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao

A Multimethod Approach to Multimodal Function Optimization Fredrik Foss, Ole Mengshoel

A NEAT-based Multiclass Classification Method with Class Binarization Zhenyu Gao, Gongjin Lan

A New Pathway to Approximate Energy Expenditure and Recovery of an Athlete Fabian Weigend, Jason Siegler, Oliver Obst

A Niching Framework based on Fitness Proportionate Sharing for Multi-Objective Genetic Algorithm (MOGA- FPS) Abdul-Rauf Nuhu, Xuyang Yan, Daniel Opoku, Abdollah Homaifar

A Transfer Learning Based Evolutionary Deep Learning Framework to Evolve Convolutional Neural Networks Bin Wang, Bing Xue, Mengjie Zhang 94 Poster Session

ALF – A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks Rune Krauss, Marcel Merten, Mirco Bockholt, Rolf Drechsler

ARCH-Elites: Quality-Diversity for Urban Design Theodoros Galanos, Antonios Liapis, Georgios Yannakakis, Reinhard Koenig

Ad hoc Teaming Through Evolution Joshua Cook, Kagan Tumer

Adaptive Multi-Fitness Learning for Robust Coordination Connor Yates, Ayhan Aydeniz, Kagan Tumer

Adopting Lexicase Selection for Michigan-Style Learning Classifier Systems with Continuous-Valued Inputs Alexander Wagner, Anthony Stein

Adversarial Bandit Gene Expression Programming for Symbolic Regression Congwen Xu, Qiang Lu, Jake Luo, Zhiguang Wang

Affine OneMax Arnaud Berny

An Approximate MIP-DoM Calculation for Multi-objective Optimization using Affinity Propagation Clustering Algorithm Cláudio Lopes, Flávio Martins, Elizabeth Wanner, Kalyanmoy Deb

An Evolutionary Approach to Interpretable Learning Jake Robertson, Ting Hu

An Optimal Oil Skimmer Assignment Based on a Genetic Algorithm with Minimal Mobilized Locations Dong-Hee Cho, Yong-Hyuk Kim

Ant Colony Optimization for Energy-Efficient Train Operations Federico Naldini, Paola Pellegrini, Joaquin Rodrigurez

Ant Swarm Algorithm for Self-Organizing Complex System Juntao Zhang, Peng Cheng

Automated Configuration of Parallel Machine Dispatching Rules by Machine Learning Georg Faustmann, Christoph Mrkvicka, Nysret Musliu, Felix Winter

Automated Feature Detection of Black-Box Continuous Search-Landscapes using Neural Image Recognition Boris Yazmir, Ofer Shir

Automated Software Testing and Debugging Geoff Nitschke Automatic Exploration of the Property Space of Reservoirs Mika Ito, Leo Cazenille, Nathanael Aubert-Kato

Bridging Kriging Believer and Expected Improvement Using Bump Hunting for Expensive Black-box Optimization Bing Wang, Hemant Singh, Tapabrata Ray

CMA-ES with Coordinate Selection for High-Dimensional and Ill-Conditioned Functions Hiroki Shimizu, Masashi Toyoda Poster Session 95

Comparing lifetime learning methods for morphologically evolving robots Fuda Diggelen, Guszti Eiben, Eliseo Ferrante

Continuous Encoding for Community Detection in Complex Networks Wei Zheng, Yiqing Zhang, Jianyong Sun

Dealing With a Problematic Roundabout by Optimizing a Traffic Light System Through Evolutionary Computation Francisco Cruz-Zelante, Eduardo Segredo, Gara Miranda

Designing Fitness Functions for Odour Source Localisation João Macedo, Lino Marques, Ernesto Costa

Detecting Anomalies in Spacecraft Telemetry Using Evolutionary Thresholding and LSTMs Pawel Benecki, Szymon Piechaczek, Daniel Kostrzewa, Jakub Nalepa

Diagnosing Autonomous Vehicle Driving Criteria with an Adversarial Evolutionary Algorithm Mark Coletti, Shang Gao, Spencer Paulissen, Quentin Haas, Robert Patton

Disease Outbreaks: Tuning Predictive Machine Learning Geoff Nitschke, Amina Abdullahi

Distributed Evolutionary Design of HIFU Treatment Plans Jakub Chlebik, Jiri Jaros

Dynamic Adaptation of Decomposition Vector Set Size for MOEA/D Yuta Kobayashi, Claus Aranha, Tetsuya Sakurai

Effective Recombination Operators for the family of Vehicle Routing Problems Piotr Cybula, Andrzej Jaszkiewicz, Przemysław Pełka, Marek Rogalski, Piotr Sielski

Elo-based Similar-Strength Opponent Sampling for Multiobjective Competitive Coevolution Sean Harris, Daniel Tauritz

Empirical Analysis of Variance for Genetic Programming based Symbolic Regression Lukas Kammerer, Gabriel Kronberger, Stephan Winkler

Empirical Study of Correlations in the Fitness Landscapes of Combinatorial Optimization Problems Longfei Zhang, Ke Li, Shi Gu

Environmental Impact on Evolving Language Diversity Geoff Nitschke, Gregory Furman

Error Function Learning with Interpretable Compositional Networks for Constraint-Based Local Search Florian Richoux, Jean-François Baffier

Estimation of von Mises-Fisher Distribution Algorithm, with application to support vector classification Adetunji Ajimakin, V. Susheela Devi

Evo-RL: Evolutionary-Driven Reinforcement Learning Ahmed Hallawa, Thorsten Born, Anke Schmeink, Guido Dartmann, Arne Peine, Lukas Martin, Giovanni Iacca, A.E. Eiben, Gerd Ascheid

EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text Mariele Motta, Tanja Hagemann, Sebastian Fischer, Felix Assion 96 Poster Session

Evolved Response Thresholds Generalize Across Problem Instances for a Deterministic-Response Multiagent System H. Mathias, Annie Wu, Daniel Dang

Evolving Local Interpretable Model-agnostic Explanations for Deep Neural Networks in Image Classification Bin Wang, Wenbin Pei, Bing Xue, Mengjie Zhang

Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming Henrik Mettler, Maximilian Schmidt, Walter Senn, Mihai Petrovici, Jakob Jordan Evolving Potential Field Parameters For Deploying UAV-based Two-hop Wireless Mesh Networks Rahul Dubey, Sushil Louis

Evolving Reservoir Weights in the Frequency Domain Sebastian Basterrech, Gerardo Rubino Evolving Transformer Architecture for Neural Machine Translation Ben Feng, Dayiheng Liu, Yanan Sun

Examining Forms of Inductive Bias Towards ‘Simplicity’ in Genetic Algorithms to Enhance Evolvability of Boolean Functions Hetvi Jethwani, Sumeet Agarwal

Explainability and Performance of Anticipatory Learning Classifier Systems in Non-Deterministic Environments Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet Fitness First and Fatherless Crossover William Langdon

Fitness Value Curves Prediction in the Evolutionary Process of Genetic Algorithms Renuá Almeida, Denys Ribeiro, Rodrigo Rodrigues, Otávio Teixeira

GLEAM: Genetic Learning by Extraction and Absorption of Modules Anil Saini, Lee Spector

Generating Multi-Objective Bilevel Optimization Problems with Multiple Non-Cooperative Followers Jesus-Adolfo Mejia-de-Dios, Efren Mezura-Montes

Genetic Algorithm Applied to System Identification Lucas Martinho Genetic Programming with A New Representation and A New Mutation Operator for Image Classification Qinglan Fan, Ying Bi, Bing Xue, Mengjie Zhang

Growing Simulated Robots with Environmental Feedback: an Eco-Evo-Devo Approach Kathryn Walker, Helmut Hauser, Sebastian Risi

Growth and Evolution of Deep Neural Networks from Gene Regulatory Networks Colin Flynn, Mohammed Bennamoun, Farid Boussaid

Growth and Harvest Induce Essential Dynamics in Neural Networks Ilona Kulikovskikh, Tarzan Legovi´c

Heterogeneous Agent Coordination via Adaptive Quality Diversity and Specialization Gaurav Dixit, Charles Koll, Kagan Tumer Poster Session 97

How to Evolve a Neuron Garrett Mitchener Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks Kirby Steckel, Jacob Schrum

Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots Margarita Rebolledo Coy, Daan Zeeuwe, Thomas Bartz-Beielstein, A.E. Eiben

Improved Evolution of Generative Adversarial Networks Victor Costa, Nuno Lourenço, João Correia, Penousal Machado

Improving Estimation of Distribution Genetic Programming with Novelty Initialization Christian Olmscheid, David Wittenberg, Dominik Sobania, Franz Rothlauf

Improving the Generalisation of Genetic Programming Models with Evaluation Time and Asynchronous Parallel Computing Aliyu Sani Sambo, R. Muhammad Atif Azad, Yevgeniya Kovalchuk, Vivek Padmanaabhan Indramohan, Hanifa Shah Introducing a Hash Function for the Travelling Salesman Problem for Differentiating Solutions Mehdi El Krari, Rym Guibadj, John Woodward, Denis Robilliard

It’s the Journey Not the Destination; Building Genetic Algorithms Practitioners Can Trust Jakub Vincalek, Sean Walton, Ben Evans

Labeling-Oriented Non-Dominated Sorting is Θ(MN 3) Sumit Mishra, Ved Prakash, Maxim Buzdalov

Landmark-Based Multi-Objective Route Planning for Large-scale Road Net Jiaze Sun, Nan Han, Jianbin Huang, Jiahui Deng

Learning Assignment Order In An Ant Colony Optimiser For The University Course Timetabling Problem James Sakal, Jonathan Fieldsend, Edward Keedwell

Learning Multiple Defaults for Machine Learning Algorithms Florian Pfisterer, Jan van Rijn, Philipp Probst, Andreas Mueller, Bernd Bischl

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Configuration Furong Ye, Carola Doerr, Thomas Bäck

Linear representation of categorical values Arnaud Berny

Linear-dependent Multi-interpretation Neuro-Encoded Expression Programming Jun Ma, Fenghui Gao, Shuangrong Liu, Lin Wang

MOMPA: a high performance multi-objective optimizer based on marine predator algorithm. Long Chen, Xuebing Cai, Kezhong Jin, Zhenzhou Tang

Meta-Learning for Symbolic Hyperparameter Defaults Pieter Gijsbers, Florian Pfisterer, Jan van Rijn, Bernd Bischl, Joaquin Vanschoren

Misclassification Detection based on Conditional VAE for Rule Evolution in Learning Classifier System Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama 98 Poster Session

Modeling the Evolution of Retina Neural Network Ziyi Gong, Paul Munro

Multi-Criteria Differential Evolution: Treating Multitask Optimization as Multi-Criteria Optimization Jian-Yuian Li, Ke-Jing Du, Zhi-Hui Zhan, Hua Wang, Jun Zhang

Multi-Objective Last Step Preference Bayesian Optimization Juan Ungredda, Juergen Branke, Mariapia Marchi, Teresa Montrone

Multi-objective Genetic Programming for Symbolic Regression with the Adaptive Weighted Splines Representation Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang

Neurally Guided Transfer Learning for Genetic Programming Alexander Wild, Barry Porter

Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment Xinyun Zou, Eric Scott, Alexander Johnson, Kexin Chen, Douglas Nitz, Kenneth De Jong, Jeffrey Krichmar

Neurogenetic Programming Framework for Explainable Reinforcement Learning Vadim Liventsev, Aki Härmä, Milan Petkovi´c

Novelty Particle Swarm Optimisation for Truss Optimisation Problems Hirad Assimi, Frank Neumann, Markus Wagner, Xiaodong Li

Novelty Search for Evolving Interesting Character Mechanics for a Two-Player Video Game Eirik Skjærseth, Harald Vinje, Ole Mengshoel

OPTION: OPTmization Algorithm Benchmarking ONtology Ana Kostovska, Diederick Vermetten, Carola Doerr, Sašo Džeroski, PanˇcePanov, Tome Eftimov

On detecting the novelties in metaphor-based algorithms Iztok Fister, Iztok Fister, Andres Iglesias, Akemi Galvez

On the Effectiveness of Restarting Local Search Aldeida Aleti, Mark Wallace, Markus Wagner

On the Exploitation of Neuroevolutionary Information Unai Garciarena, Nuno Lourenço, Penousal Machado, Roberto Santana, Alexander Mendiburu

On the use of feature-maps for improved quality-diversity meta-evolution David Bossens, Danesh Tarapore

Optimisation Algorithms for Parallel Machine Scheduling Problems with Setup Times Fabian Kittel, Jannik Enenkel, Jana Holznigenkemper, Neil Urquhart, Michael Guckert

Optimising Pheromone Communication in a UAV Swarm Daniel Stolfi, Matthias Brust, Grégoire Danoy, Pascal Bouvry

Optimising the Introduction of Connected and Autonomous Vehicles in a Public Transport System using Macro-Level Mobility Simulations and Evolutionary Algorithms Kate Han, Lee Christie, Alexandru-Ciprian Z˘avoianu, John McCall Poster Session 99

Optimization of Multi-Objective Mixed-Integer Problems with a Model-Based Evolutionary Algorithm in a Black-Box Setting Krzysztof Sadowski, Dirk Thierens, Peter Bosman

Optimizing a GPU-Accelerated Genetic Algorithm for the Vehicle Routing Problem Marwan Abdelatti, Abdeltawab Hendawi, Manbir Sodhi Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison Mark Connor, Michael O’Neill Partial-ACO as a GA Mutation Operator Applied to TSP Instances Darren Chitty

Pathogen Dose based Natural Killer Cell Algorithm for Classification Dongmei Wang, Yiwen Liang, Chengyu Tan, Hongbin Dong, Xinmin Yang

Permutation-based Optimization using a Generative Adversarial Network Sami Lemtenneche, Abdelhakim Cheriet, Abdellah Bensayah

Predicting soft robot’s locomotion fitness Renata Biazzi, André Fujita, Daniel Takahashi

Principled quality diversity for ensemble classifiers using MAP-Elites Kyle Nickerson, Ting Hu

Promoting Reproductive Isolation Through Diversity in On-line Collective Robotics Amine Boumaza Quantum Genetic Selection Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello Reinforcement Learning for Dynamic Optimization Problems Abdennour Boulesnane, Souham Meshoul Reinforcement Learning with Rare Significant Events: Direct Policy Search vs. Gradient Policy Search Paul Ecoffet, Nicolas Fontbonne, Jean-Baptiste André, Nicolas Bredeche Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, Martin Zaefferer Risk Aware Optimization of Water Sensor Placement Antonio Candelieri, Andrea Ponti, Francesco Archetti Scatter Search for high-dimensional feature selection using feature grouping Miguel García Torres, Diego P.Pinto Roa, José Luis Vázquez Noguera, Federico Divina, Francisco Gómez Vela, Julio C. Mello Román Selecting Between Evolutionary and Classical Algorithms for the CVRP Using Machine Learning Justin Fellers, Jose Quevedo, Marwan Abdelatti, Meghan Steinhaus, Manbir Sodhi

Selecting Miners within Blockchain-based Systems Using Evolutionary Algorithms for Energy Optimisation Akram Alofi, Mahmoud Bokhari, Robert Hendley, Rami Bahsoon

Setup Of Fuzzy Hybrid Particle Swarms: A Heuristic Approach Nicolas Roy, Charlotte Beauthier, Timotéo Carletti, Alexandre Mayer 100 Poster Session

Solving Dial-A-Ride Problems using a new Hybrid Evolutionary Algorithm Sonia Nasri Sparsity-based Evolutionary Multi-objective Feature Selection for Multi-label Classification Kaan Demir, Bach Nguyen, Bing Xue, Mengjie Zhang

Stochastic Local Search for Efficient Hybrid Feature Selection Ole Mengshoel, Tong Yu, Jon Riege, Eirik Flogard

Structural Damage Identification under Non-linear EOV Effects Using Genetic Programming Mohsen Mousavi, Amir Gandomi, Magd Wahab

The Effect of Offspring Population Size on NSGA-II: A Preliminary Study Max Hort, Federica Sarro

The Factory Must Grow: Automation in Factorio Kenneth Reid, Iliya Miralavy, Stephen Kelly, Wolfgang Banzhaf, Cedric Gondro

The Impact of Different Tasks on Evolved Robot Morphologies Matteo De Carlo, Eliseo Ferrante, Jacintha Ellers, Gerben Meynen, A. Eiben

The Optimal Filtering set Problem with Application to Surrogate Evaluation in Genetic Programming Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, Ramiro Varela

Three population co-evolution for generating mechanics of endless runner games Vojtˇech Cerný,ˇ Jakub Gemrot

Time Complexity Analysis of the Deductive Sort in the Best Case Sumit Mishra, Ved Prakash

Towards Higher Order Fairness Functionals for Smooth Path Planning Victor Parque

Towards Parameter-less Heuristics for Vehicle Routing Problems Tomasz Jastrzab, Michal Myller, Lukasz Tulczyjew, Miroslaw Blocho, Wojciech Ryczko, Michal Kawulok, Jakub Nalepa

Two Comprehensive Performance Metrics for Overcoming the Deficiencies of IGD and HV Liping Wang, Lin Zhang, Yu Ren, Qicang Qiu, Feiyue Qiu

Understanding evolutionary induction of decision trees: A multi-tree repository approach Krzysztof Jurczuk, Marcin Czajkowski, Marek Kretowski

Unit-aware Multi-objective Genetic Programming for the Prediction of the Stokes Flow around a Sphere Heiner Zille, Fabien Evrard, Sanaz Mostaghim, Berend van Wachem

Using Knowledge of Human-Generated Code to Bias the Search in Program Synthesis with Grammatical Evolution Dirk Schweim, Erik Hemberg, Dominik Sobania, Una-May O’Reilly, Franz Rothlauf

Wastewater Systems Planned Maintenance Scheduling Using Multi-Objective Optimisation Sabrina Draude, Edward Keedwell, Rebecca Hiscock, Zoran Kapelan

Weighted Ensemble of Gross Error Detection methods based on Particle Swarm Optimization Daniel Dobos, Thanh Nguyen, John McCall, Allan Wilson, Phil Stockton, Helen Corbett Poster Session 101

Younger Is Better: A Simple and Efficient Selection Strategy for MAP-Elites Alex Coninx, Stephane Doncieux

Abstracts by Track 104 Abstracts

Ant Colony Optimization and Swarm Intelligence

ACO-SI 1 Computational swarm intelligence has been demonstrably shown to efficiently solve high-dimensional optimization prob- Monday, July 12, 14:20-15:40 Room ACO-SI lems due to its flexibility, robustness, and (low) computational A hybrid ant colony optimization algorithm for the cost. Despite these features, swarm-based algorithms are black knapsack problem with a single continuous variable boxes whose dynamics may be hard to understand. In this paper, we delve into the Fish School Search (FSS) algorithm by Xinhua Yang, College of Computer and Information Science, looking at how fish interact within the fish school. We find that Fujian Agriculture and Forestry University; Key Laboratory of the network emerging from these interactions is structurally Smart Agriculture and Forestry (Fujian Agriculture and Forestry invariant to the optimization of three benchmark functions: University), Yufan Zhou, College of Computer and Information Rastrigin, Rosenbrock and Schwefel. However, at the same Science, Fujian Agriculture and Forestry University; Key Lab- time, our results also reveal that the level of social interactions oratory of Smart Agriculture and Forestry (Fujian Agriculture among the fish depends on the problem. We show that the and Forestry University), Ailing Shen, College of Computer and absence of highly influential fish leads to a slow-paced con- Information Science, Fujian Agriculture and Forestry Univer- vergence in FSS and that the changes in the intensity of social sity; Key Laboratory of Smart Agriculture and Forestry (Fujian interactions enable good performance on both unimodal and Agriculture and Forestry University), Juan Lin, College of Com- multimodal problems. Finally, we examine two other swarm- puter and Information Science, Fujian Agriculture and Forestry based algorithms—the Artificial Bee Colony (ABC) and Particle University; Key Laboratory of Smart Agriculture and Forestry Swarm Optimization (PSO) algorithms—and find that for the (Fujian Agriculture and Forestry University), Yiwen Zhong, Col- same three benchmark functions, the structural invariance lege of Computer and Information Science, Fujian Agriculture characteristic only occurs in the FSS algorithm. We argue that and Forestry University; Key Laboratory of Smart Agriculture FSS, ABC, and PSO have distinctive signatures of interaction and Forestry (Fujian Agriculture and Forestry University) structure and flow. Knapsack Problem with single Continuous variable (KPC) is a natural generalization of the standard 0-1 Knapsack Problem. Ants can solve the parallel drone scheduling traveling In KPC, the capacity of the knapsack is not fixed, so it becomes salesman problem more difficult to solve. Although several metaheuristics have Quoc Trung Dinh, ORLab, Phenikaa University, Duc Dong Do, been proposed to solve the KPC, their performances are not VNU University of Engineering and Technology, Minh Hoàng stable enough. This paper proposes a hybrid Ant Colony Op- Hà, ORLab, Phenikaa University timization (HACO) algorithm to solve the KPC. In the HACO algorithm, a simplified ACO algorithm is designed to search the In this work, we are interested in studying the parallel drone optimal subset of items, and the mutation operator of differen- scheduling traveling salesman problem (PDSTSP), where deliv- tial evolution algorithm is used to search the optimal value of eries are split between a truck and a fleet of drones. The truck continuous variable. The parameter of the mutation operator performs a common delivery tour, while the drones are forced is adaptively configured via an ACO algorithm. In the simpli- to perform back and forth trips between customers and a depot. fied ACO algorithm, an independent selection strategy and a The objective is to minimize the completion time coming back pheromone-based selection strategy are designed to speed up to the depot of all the vehicles. We present a hybrid ant colony the algorithm and simplify the tuning of parameters respec- optimization (HACO) metaheuristic to solve the problem. Our tively. Personal best solutions constructed by each ant are used algorithm is based on an idea from the literature that represents to deposit pheromone. Furthermore, a value-based greedy a PDSTSP solution as a permutation of all customers. And then optimization strategy is introduced to enhance the solutions. a dynamic programming is used to decompose the customer The HACO algorithm is experimentally analyzed on 40 KPC sequence into a tour for the truck and trips for the drones. We instances. The experimental results show that the HACO algo- propose a new dynamic programming combined with other rithm is better than other competitors in terms of obtaining problem-tailored components to efficiently solve the problem. the best solution and the mean solution. When being tested on benchmark instances from the literature, the HACO algorithm outperforms state-of-the-art algorithms in Fishing for Interactions: A Network Science Approach to terms of both running time and solution quality. More remark- Modeling Fish School Search ably, we find 23 new best known solutions out of 90 instances Mariana Macedo, University of Exeter, Lydia Taw, George Fox considered. University, Nishant Gurrapadi, University of Texas, Rodrigo Lira, University of Pernambuco, Diego Pinheiro, Catholic Uni- Stasis type particle stability in a stochastic model of particle versity of Pernambuco, Marcos Oliveira, University of Exeter, swarm optimization Carmelo Bastos-Filho, University of Pernambuco, Ronaldo Tomasz Kulpa, Cardinal Stefan Wyszy´nski University in Warsaw, Menezes, University of Exeter Krzysztof Trojanowski, Cardinal Stefan Wyszy´nski University in Abstracts 105

Warsaw, Krzysztof Wójcik, Cardinal Stefan Wyszy´nski Univer- University, Raina Zakir, Middlesex University Dubai, Muhanad sity in Warsaw Alkilabi, University of Namur, Elio Tuci, University of Namur, Particle Swarm Optimization (PSO) is a heuristic optimization Eliseo Ferrante, VU University Amsterdam, Technology Innova- technique where particles explore optima of the fitness land- tion Institute scape defined in the search space. It is a practical approach In collective decision-making, individuals in a swarm reach but vulnerable to incorrect particle movement parameter tun- consensus on a decision using only local interactions without ing. Therefore, stochastic models of a particle and a swarm any centralized control. In the context of the best-of-n problem are subject to analysis. In this paper, we study the stability - characterized by n discrete alternatives - it has been shown properties of particles controlled by a universal update equa- that consensus to the best option can be reached if individuals tion proposed by Cleghorn and Engelbrecht. We propose a disseminate that option more than the other options. Besides new concept of particle state, namely the stasis state when the being used as a mechanism to modulate positive feedback, difference between two consecutive particle location variance long dissemination times could potentially also be used in an values is not changing much after a sufficient time. We also adversarial way, whereby adversarial swarms could infiltrate propose a convergence time measure, representing a minimal the system and propagate bad decisions using aggressive dis- number of steps a particle location variance needs to reach sta- semination strategies. Motivated by the above scenario, in this sis. Finally, we visualize the convergence time characteristics paper we propose a bio-inspired defence strategy that allows obtained in simulations. the swarm to be resilient against options that can be dissemi- nated for longer times. This strategy mainly consists in reduc- ACO-SI 2 ing the mobility of the agents that are associated to options disseminated for a shorter amount of time, allowing the swarm Tuesday, July 13, 12:20-13:40 Room ACO-SI to converge to this option. We study the effectiveness of this The Paradox of Choice in Evolving Swarms: Information strategy using two classical decision mechanisms, the voter Overload Leads to Limited Sensing model and the majority rule, showing that the majority rule is necessary in our setting for this strategy to work. The strategy Calum Imrie, University of Edinburgh, Michael Herrmann, Uni- has also been validated on a real Kilobots experiment. versity of Edinburgh, Olaf Witkowski, Cross Labs The paradox of choice refers to the observation that numer- ous choices can have a detrimental effect on the quality of ACO-SI 3 + ENUM 1 + THEORY 1 - BP decision making. We study this effect in swarms in the con- text of a resource foraging task. We simulate the evolution of Tuesday, July 13, 14:20-15:40 Rooms ACO-SI, ENUM, swarms gathering energy from a number of energy sources THEORY distributed in a two-dimensional environment. As the num- A Rigorous Runtime Analysis of the 2-MMAS on Jump ber of sources increases, the evolution of the swarm results Functions: Ant Colony Optimizer Can Cope Well With Local in reduced levels of efficiency, despite the sources covering Optima more space. Our results indicate that this effect arises because the simultaneous detection of multiple sources is incompat- Riade Benbaki, École Polytechnique, Ziyad Benomar, École ible with an evolutionary scheme that favours greedy energy Polytechnique, Benjamin Doerr, Ecole Polytechnique, Labora- consumption. In particular, the communication among the toire d’Informatique (LIX) agents tends to reduce their efficiency by precluding the evolu- Ant colony optimizers have been successfully used as general- tion of a clear preference for an increasing number of options. purpose optimization heuristics. Due to the complicated na- The overabundance of explicit information in the swarm about ture of the random processes that describe the runs of ACO al- fitness-related options cannot be exploited by the agents lack- gorithms, the mathematical understanding of these algorithms ing complex planning capabilities. If the sensor ranges evolve is much less developed than that of other nature-inspired in addition to the behaviour of the agents, a preference for re- heuristics. In this first runtime analysis of a basic ACO al- duced choice results, and the average range approaches zero gorithm on a classic multimodal benchmark, we analyze the as the number of sources increases. Our study thus presents a runtime of the 2-MMASib on jump functions. For moderate minimal model for the paradox of choice, which implies several jump sizes k α0 lnn, α0 0 a constant, we prove a run- ≤ > options of experimental verification. time of order O(pn/ρ), when the evaporation factor ρ satis- fies ρ Cn 1/2 ln(n) 1 for a sufficiently small constant C. For ≤ − − A Bio-Inspired Spatial Defence Strategy for Collective ρ Θ(n 1/2 ln(n) 1), we thus obtain a runtime of O(n ln(n)). = − − Decision Making in Self-Organized Swarms This result shows that simple ACO algorithms can cope much Judhi Prasetyo, Middlesex University Dubai, University of Na- better with local optima than many evolutionary algorithms, mur, Giulia De Masi, Technology Innovation Institute, Khalifa which need Ω(nk ) time. 106 Abstracts

Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)

CS 1 - BP controller of VSRs for the task of locomotion. We investigate ex- perimentally whether two key factors—evolutionary algorithm Monday, July 12, 10:20-11:40 Room CS (EA) and representation—impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise a Sparse Reward Exploration via Novelty Search and way for measuring biodiversity, systematically characterizing Emitters the robots shape and behavior, and apply it to the VSRs evolved Giuseppe Paolo, Sorbonne University, Softbank Robotics with three EAs and two representations. The experimental re- Europe, Alexandre Coninx, Sorbonne University, Stephane sults suggest that the representation matters more than the EA Doncieux, Sorbonne University, Alban Laflaquière, Softbank and that there is not a clear trade-off between diversity and Robotics Europe effectiveness. Reward-based optimization algorithms require both explo- ration, to find rewards, and exploitation, to maximize perfor- The Environment and Body-Brain Complexity mance. The need for efficient exploration is even more signifi- Geoff Nitschke, University of Cape Town, Christina Spanellis, cant in sparse reward settings, in which performance feedback University of Cape Town, Brooke Stewart, University of Cape is given sparingly, thus rendering it unsuitable for guiding the Town search process. In this work, we introduce the SparsE Reward An open question for both natural and artificial evolutionary Exploration via Novelty and Emitters (SERENE) algorithm, ca- systems is how, and under what environmental and evolution- pable of efficiently exploring a search space, as well as opti- ary conditions complexity evolves. This study investigates the mizing rewards found in potentially disparate areas. Contrary impact of increasingly complex task environments on the evo- to existing emitters-based approaches, SERENE separates the lution of robot complexity. Specifically, the impact of evolving search space exploration and reward exploitation into two al- body-brain couplings on locomotive task performance, where ternating processes. The first process performs exploration robot evolution was directed by either body-brain exploration through Novelty Search, a divergent search algorithm. The sec- (novelty search) or objective-based (fitness function) evolu- ond one exploits discovered reward areas through emitters, i.e. tionary search. Results indicated that novelty search enabled local instances of population-based optimization algorithms. the evolution of increased robot body-brain complexity and A meta-scheduler allocates a global computational budget by efficacy given specific environment conditions. The key contri- alternating between the two processes, ensuring the discovery bution is thus the demonstration that body-brain exploration is and efficient exploitation of disjoint reward areas. SERENE re- suitable for evolving robot complexity that enables high fitness turns both a collection of diverse solutions covering the search robots in specific environments. space and a collection of high-performing solutions for each distinct reward area. We evaluate SERENE on various sparse re- ward environments and show it compares favorably to existing CS 2 baselines. Tuesday, July 13, 14:20-15:40 Room CS

Biodiversity in Evolved Voxel-based Soft Robots On the Impact of Tangled Program Graph Marking Schemes Eric Medvet, DIA, University of Trieste, Italy, Federico Pigozzi, under the Atari Reinforcement Learning Benchmark DIA, University of Trieste, Italy, Alberto Bartoli, DIA, University Alexandru Ianta, Dalhousie University, Ryan Amaral, Dalhousie of Trieste, Italy, Marco Rochelli, DIA, University of Trieste, Italy University, Caleidgh Bayer, Dalhousie University, Robert In many natural environments, there are different forms of Smith, Dalhousie University, Malcolm Heywood, Dalhousie living creatures that successfully accomplish the same task University while being diverse in shape and behavior. This biodiversity Tangled program graphs (TPG) support emergent modularity is what made life capable of adapting to disrupting changes. by first identifying subsets of programs that can usefully coexist Being able to reproduce biodiversity in non-biological agents, (a team/ graph node) and then identifying the circumstance un- while still optimizing them for a particular task, might increase der which to reference other teams (arc adaptation). Variation their applicability to scenarios where human response to un- operators manipulate the content of teams and arcs. This intro- expected changes is not possible. In this work, we focus on duces cycles into the TPG structures. Previously, this effect was Voxel-based Soft Robots (VSRs), a form of robots that grants eradicated at run time by marking nodes while evaluating TPG great freedom in the design of both body and controller and individuals. In this work, a new marking heuristic is introduced, is hence promising in terms of biodiversity. We use evolution- that of arc (learner) marking. This means that nodes can be re- ary computation for optimizing, at the same time, body and visited, but not the same arcs. We investigate the impact of this Abstracts 107

through 18 titles from the Arcade Learning Environment. The fun and have more human-like design than levels produced by performance and complexity of the policies appear to be simi- one GAN. lar, but with specific tasks (game titles) resulting in preferences for one scheme over another. Resource Availability and The Evolution of Cooperation in a 3D Agent-Based Simulation MAEDyS: Multiagent Evolution via Dynamic Skill Selection Lara Dal Molin, University of St Andrews, Jasmeen Kanwal, University of St Andrews, Christopher Stone, University of St Enna Sachdeva, Oregon State University, Shauharda Khadka, Andrews Microsoft, Somdeb Majumdar, Intel Corporation, Kagan Tumer, Oregon State University There is evidence of a relationship between the dynamics of re- source availability and the evolution of cooperative behaviour Evolving effective coordination strategies in tightly coupled in complex networks. Previous studies have used mathematical multiagent settings with sparse team fitness evaluations is chal- models, agent-based models, and studies of hunter-gatherer lenging. It relies on multiple agents simultaneously stumbling societies to investigate the causal mechanisms behind this re- upon the goal state to generate a learnable feedback signal. In lationship. Here, we present a novel, agent-based software such settings, estimating an agent’s contribution to the over- system, built using Unity 3D, which we employ to investigate all team performance is extremely difficult, leading to a well- the adaptation of food sharing networks to fluctuating resource known structural credit assignment problem. This problem is availability. As a benefit of using Unity, our system possesses further exacerbated when agents must complete sub-tasks with an easily customisable, visually compelling interface where added spatial and temporal constraints, and different sub-tasks evolution can be observed in real-time. Across four types of may require different local skills. We introduce MAEDyS, Multi- populations, under three environmental conditions, we per- agent Evolution via Dynamic Skill Selection, a hybrid bi-level formed a quantitative analysis of the evolving structure of social optimization framework that augments evolutionary methods interactions. A biologically-inspired gene-sequencing function with policy gradient methods to generate effective coordina- translates an arbitrarily extendable genome into phenotypic tion policies. MAEDyS learns to dynamically switch between behaviour. Our results contribute to the understanding of how multiple local skills towards optimizing the team fitness. It resource availability affects the evolutionary path taken by arti- adopts fast policy gradients to learn several local skills using ficial societies. It emerges that environmental conditions have dense local rewards. It utilizes an evolutionary process to opti- a greater impact on social evolution compared to the initial ge- mize the delayed team fitness by ’recruiting’ the most optimal netic configurations of each society. In particular, we find that skill at any given time. The ability to switch between various scenarios of periodically fluctuating resources lead to the evo- local skills during an episode eliminates the need for designing lution of stable, tightly organised societies, which form small, heuristic mixing functions. We evaluate MAEDyS in complex local, mutualistic food-sharing networks. multiagent coordination environments with spatial and tem- poral constraints and show that it outperforms prior methods. CS 3 Using Multiple Generative Adversarial Networks to Build Wednesday, July 14, 10:20-11:40 Room CS Better-Connected Levels for Mega Man Benjamin Capps, Southwestern University, Jacob Schrum, Multi-Emitter MAP-Elites: Improving quality, diversity and Southwestern University data efficiency with heterogeneous sets of emitters Generative Adversarial Networks (GANs) can generate levels Antoine Cully, Imperial College London for a variety of games. This paper focuses on combining GAN- Quality-Diversity (QD) optimisation is a new family of learn- generated segments in a snaking pattern to create levels for ing algorithms that aims at generating collections of diverse Mega Man. Adjacent segments in such levels can be orthog- and high-performing solutions. Among those algorithms, the onally adjacent in any direction, meaning that an otherwise recently introduced Covariance Matrix Adaptation MAP-Elites fine segment might impose a barrier between its neighbor de- (CMA-ME) algorithm proposes the concept of emitters, which pending on what sorts of segments in the training set are being uses a predefined heuristic to drive the algorithm�s explo- most closely emulated: horizontal, vertical, or corner segments. ration. This algorithm was shown to outperform MAP-Elites, a To pick appropriate segments, multiple GANs were trained popular QD algorithm that has demonstrated promising results on different types of segments to ensure better flow between in numerous applications. In this paper, we introduce Multi- segments. Flow was further improved by evolving the latent Emitter MAP-Elites (ME-MAP-Elites), an algorithm that directly vectors for the segments being joined in the level to maximize extends CMA-ME and improves its quality, diversity and data the length of the level�s solution path. Using multiple GANs efficiency. It leverages the diversity of a heterogeneous set of to represent different types of segments results in significantly emitters, in which each emitter type improves the optimisa- longer solution paths than using one GAN for all segment types, tion process in different ways. A bandit algorithm dynamically and a human subject study verifies that these levels are more finds the best selection of emitters depending on the current 108 Abstracts

situation. We evaluate the performance of ME-MAP-Elites on dynamics as well as potential advantages over archive-based six tasks, ranging from standard optimisation problems (in 100 NS in terms of time complexity. dimensions) to complex locomotion tasks in robotics. Our comparisons against CMA-ME and MAP-Elites show that ME- Ensemble Feature Extraction for Multi-Container MAP-Elites is faster at providing collections of solutions that Quality-Diversity Algorithms are significantly more diverse and higher performing. More- Leo Cazenille, Ochanomizu University over, in cases where no fruitful synergy can be found between the different emitters, ME-MAP-Elites is equivalent to the best Quality-Diversity algorithms search for large collections of di- of the compared algorithms. verse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are spe- Monte Carlo Elites: Quality-Diversity Selection as a cially adapted for multi-modal problems that can be solved in Multi-Armed Bandit Problem many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly de- Konstantinos Sfikas, University of Malta, Antonios Liapis, Uni- pendent on the choice of feature descriptors (FDs) quantifying versity of Malta, Georgios Yannakakis, University of Malta, the similarity in behaviour of the solutions. While FDs usu- Technical University of Crete ally needs to be hand-designed, recent studies have proposed A core challenge of evolutionary search is the need to balance ways to define them automatically by using feature extraction between exploration of the search space and exploitation of techniques, such as PCA or Auto-Encoders, to learn a repre- highly fit regions. Quality-diversity search has explicitly walked sentation of the problem from previously explored solutions. this tightrope between a population’s diversity and its qual- Here, we extend these approaches to more complex problems ity. This paper extends a popular quality-diversity search al- which cannot be efficiently explored by relying only on a single gorithm, MAP-Elites, by treating the selection of parents as a representation but require instead a set of diverse and comple- multi-armed bandit problem. Using variations of the upper- mentary representations. We describe MC-AURORA, a Quality- confidence bound to select parents from under-explored but Diversity approach that optimises simultaneously several col- potentially rewarding areas of the search space can accelerate lections of solutions, each with a different set of FDs, which are, the discovery of new regions as well as improve its archive’s in turn, defined automatically by an ensemble of modular auto- total quality. The paper tests an indirect measure of quality encoders. We show that this approach produces solutions that for parent selection: the survival rate of a parent’s offspring. are more diverse than those produced by single-representation Results show that maintaining a balance between exploration approaches. and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds. CS 4 BR-NS: an Archive-less Approach to Novelty Search Wednesday, July 14, 12:20-13:40 Room CS Achkan Salehi, Sorbonne University, Alexandre Coninx, Sor- bonne University, Stephane Doncieux, Sorbonne University Evolving Soft Robotic Jamming Grippers As open-ended learning based on divergent search algorithms Seth Fitzgerald, CSIRO, Data61, Robotics and Autonomous Sys- such as Novelty Search (NS) draws more an more attention tems Group; QUT, Engineering Faculty, EER School, Gary De- from the research community, it is natural to expect that its laney, CSIRO, Data61, David Howard, CSIRO, Data61, Robotics application to increasingly complex real-world problems will and Autonomous Systems Group, Frederic Maire, QUT, Engi- require the exploration to operate in higher dimensional Be- neering Faculty, EER School havior Spaces which wont necessarily be Euclidean. Novelty Jamming Grippers are a novel class of soft robotic actuators Search traditionally relies on knn search and an archive of pre- that can robustly grasp and manipulate objects of arbitrary viously visited behavior descriptors which are assumed to live shape. They are formed by placing a granular material within in Euclidean space. This is problematic because of a number a flexible skin connected to a vacuum pump and function by of issues. First, Euclidean distance and knn search are known pressing the unjammed gripper against a target object and to behave differently and become less meaningful in high di- evacuating the air to transition the material to a jammed (solid) mensional spaces. Second, the archive has to be bounded state, gripping the target object. However, due to the com- since the computational complexity of finding nearest neigh- plex interactions between grain morphology and target object bors in that archive grows linearithmically with its size. A sub- shape, much uncertainty still remains regarding optimal con- optimal bound can result in “cycling’’, which inhibits stituent grain shapes for specific gripping applications. We exploration progress. In this paper, we discuss an alternative address this challenge by utilising a modern Evolutionary Al- approach to novelty estimation, which doesn�t require an gorithm, NSGA-III, combined with a Discrete Element Method archive, makes no assumption on the metrics that can be de- soft robot model to perform a multi-objective optimisation of fined in the behavior space and does not rely on knn search. grain morphology for use in jamming grippers for a range of We conduct experiments to gain insight into its feasibility and target object sizes. Our approach optimises the microscopic Abstracts 109

properties of the system to elicit bespoke functional granu- agents controlled by Continuous-Time Recurrent Neural Net- lar material performance driven by the complex relationship works, which are optimized by means of neuro-evolution. We between the individual particle morphologies and the related characterize signal decoding as either regression or classifica- emergent behaviour of the bulk state. Results establish the tion, with limited and unlimited signal amplitude. First, we important contribution of grain morphology to gripper perfor- show how this choice affects the complexity of the evolutionary mance and the critical role of local surface curvature and the search, and leads to different levels of generalization. We then length scale governed by the relative sizes of the grains and assess the effect of noise, and test the evolved signaling system target object. in a referential game. In various settings, we observe agents evolving to share a dictionary of symbols, with each symbol spontaneously associated to a 1-D unique signal. Finally, we Evolving Gaits for Damage Control in a Hexapod Robot analyze the constellation of signals associated to the evolved Geoff Nitschke, University of Cape Town, Christopher Mailer, signaling systems and note that in most cases these resemble a University of Cape Town, Leanne Raw, University of Cape Town Pulse Amplitude Modulation system. Autonomous robots are increasingly used in remote and haz- ardous environments, where damage to sensory-actuator sys- Seeking Quality Diversity in Evolutionary Co-design of tems cannot be easily repaired. Such robots must therefore Morphology and Control of Soft Tensegrity Modular Robots have controllers that continue to function effectively given un- Enrico Zardini, University of Trento, Davide Zappetti, EPFL, expected malfunctions and damage to robot morphology. This Davide Zambrano, EPFL, Giovanni Iacca, University of Trento, study applies the Intelligent Trial and Error (IT&E) algorithm to Dario Floreano, EPFL adapt hexapod robot control to various leg failures and demon- Designing optimal soft modular robots is difficult, due to non- strates the IT&E map-size parameter as a critical parameter trivial interactions between morphology and controller. Evo- in influencing IT&E adaptive task performance. We evaluate lutionary algorithms (EAs), combined with physical simula- robot adaptation for multiple leg failures on two different map- tors, represent a valid tool to overcome this issue. In this sizes in simulation and validate evolved controllers on a phys- work, we investigate algorithmic solutions to improve the Qual- ical hexapod robot. Results demonstrate a trade-off between ity Diversity of co-evolved designs of Tensegrity Soft Modular adapted gait speed and adaptation duration, dependent on Robots (TSMRs) for two robotic tasks, namely goal reaching adaptation task complexity (leg damage incurred), where map- and squeezing trough a narrow passage. To this aim, we use size is crucial for generating behavioural diversity required for three different EAs, i.e., MAP-Elites and two custom algorithms: adaptation. one based on Viability Evolution (ViE) and NEAT (ViE-NEAT), the other named Double Map MAP-Elites (DM-ME) and de- A Signal-Centric Perspective on the Evolution of Symbolic vised to seek diversity while co-evolving robot morphologies Communication and neural network (NN)-based controllers. In detail, DM-ME extends MAP-Elites in that it uses two distinct feature maps, Quintino Lotito, University of Trento, Leonardo Custode, Uni- referring to morphologies and controllers respectively, and in- versity of Trento, Giovanni Iacca, University of Trento tegrates a mechanism to automatically define the NN-related The evolution of symbolic communication is a longstanding feature descriptor. Considering the fitness, in the goal-reaching open research question in biology. While some theories sug- task ViE-NEAT outperforms MAP-Elites and results equivalent gest that it originated from sub-symbolic communication (i.e., to DM-ME. Instead, when considering diversity in terms of iconic or indexical), little experimental evidence exists on how "illumination" of the feature space, DM-ME outperforms the organisms can actually evolve to define a shared set of sym- other two algorithms on both tasks, providing a richer pool of bols with unique interpretable meaning, thus being capable possible robotic designs, whereas ViE-NEAT shows comparable of encoding and decoding discrete information. Here, we use performance to MAP-Elites on goal reaching, although it does a simple synthetic model composed of sender and receiver not exploit any map.

Evolutionary Combinatorial Optimization and Metaheuristics

ECOM 1 In the area of evolutionary computation the calculation of di- verse sets of high-quality solutions to a given optimization Monday, July 12, 10:20-11:40 Room ECOM problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into Evolutionary Diversity Optimization and the Minimum the working principles of baseline evolutionary algorithms for Spanning Tree Problem diversity optimization are still rare. In this paper we study the Jakob Bossek, University of Münster, Frank Neumann, The well-known Minimum Spanning Tree problem (MST) in the University of Adelaide 110 Abstracts

context of diversity optimization where population diversity is problem with model-generated instances having a power-law measured by the sum of pairwise edge overlaps. Theoretical distribution. Specifically, we target the regions in which com- results provide insights into the fitness landscape of the MST putational difficulty undergoes an easy/hard phase transition diversity optimization problem pointing out that even for a as a function of clause density and of the power-law exponent. population of µ 2 fitness plateaus (of constant length) can Our approach makes use of a sampling technique to build a = be reached, but nevertheless diverse sets can be calculated in graph model (a local optima network) in which nodes are lo- polynomial time. We supplement our theoretical results with cal optima and directed edges are transitions between optima a series of experiments for the unconstrained and constraint basins. The objective is to relate the structure of the instance fit- case where all solutions need to fulfill a minimal quality thresh- ness landscape with problem difficulty through the transition. old. Our results show that a simple (µ 1)-EA can effectively We succeed in associating the transition with straightforward + compute a diversified population of spanning trees of high network metrics, thus providing a novel and original fitness quality. landscape view of the computational features of the power-law model and its phase transition. A Two-Stage Multi-Objective Genetic Programming with Archive for Uncertain Capacitated Arc Routing Problem Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Shaolin Wang, Victoria University of Wellington, Yi Mei, Victo- Functions ria University of Wellington, Mengjie Zhang, Victoria University of Wellington Aneta Neumann, The University of Adelaide, Jakob Bossek, University of Muenster, Frank Neumann, The University of Genetic Programming Hyper-Heuristic (GPHH) is a promising Adelaide technique to automatically evolve effective routing policies to handle the uncertain environment in the Uncertain Capaci- Submodular functions allow to model many real-world optimi- tated Arc Routing Problem (UCARP). Previous studies mainly sation problems. This paper introduces approaches for com- focus on the effectiveness of the evolved routing policies, but puting diverse sets of high quality solutions for submodular the size is ignored. This paper aims to develop new GPHH optimisation problems with uniform and knapsack constraints. methods to optimise the effectiveness and the size simultane- We first present diversifying greedy sampling approaches and ously. There are two challenges. First, it is much easier for GP analyse them with respect to the diversity measured by entropy to generate small but ineffective individuals than effective ones, and the approximation quality of the obtained solutions. Af- thus the search can be easily stuck with small but ineffective terwards, we introduce an evolutionary diversity optimisation individuals. Second, the effectiveness evaluation in GPHH is (EDO) approach to further improve diversity of the set of so- stochastic, making it challenging to identify and retain effective lutions. We carry out experimental investigations on popular individuals. To address these issues, we develop a Two-Stage submodular benchmark problems and analyse trade-offs in Multi-Objective GP algorithm with Archive (TSNSGPII-a). The terms of solution quality and diversity of the resulting solution two-stage framework addresses the bias towards the size. The sets. external archive stores potentially effective individuals that may be lost during the evolution, and reuses them to gener- ECOM 2 - BP ate offspring. The experimental results show that TSNSGPII-a Monday, July 12, 14:20-15:40 Room ECOM can obtain significantly better routing policies than the exist- ing state-of-the-art approaches in terms of both effectiveness A Graph Coloring based Parallel Hill Climber for and size. If selecting the most effective routing policy from Large-scale NK-landscapes the Pareto front, TSNSGPII-a obtains significantly smaller rout- Bilel Derbel, Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 ing policies with statistically comparable or significantly better CRIStAL, Lorenzo Canonne, Univ. Lille, Inria, CNRS, Centrale effectiveness. Lille, UMR 9189 CRIStAL Efficient hill climbers are at the heart of the latest gray-box Real-like MAX-SAT Instances and the Landscape Structure optimization techniques, where some structural information Across the Phase Transition about the optimization problem is available. Focusing on Francisco Chicano, University of Malaga, Gabriela Ochoa, Uni- NK-landscapes as a challenging class of k-bounded pseudo- versity of Stirling, Marco Tomassini, University of Lausanne boolean functions, we propose a quality-enhanced and a time- In contrast with random uniform instances, industrial SAT in- accelerated hill climber. Our investigations are based on the stances of large size are solvable today by state-of-the-art algo- idea of performing several simultaneous moves at each itera- rithms. It is believed that this is the consequence of the non- tion of the search process. This is enabled using graph coloring random structure of the distribution of variables into clauses. to structure the interacting variables of an NK-landscape, and In order to produce benchmark instances resembling those of to identify subsets of possibly improving independent moves real-world formulas with a given structure, generative models in the Hamming distance 1 neighborhood. Besides being ex- have been proposed. In this paper we study the MAX-3SAT tremely competitive with respect to the state-of-the-art first- Abstracts 111

and best- ascent serial variants, our initial design exposes a the original version. Another benefit of UMM is that its compu- natural degree of parallelism allowing us to convert our serial tational complexity increases linearly with both the number of algorithm into a parallel hill climber without further design function evaluations and the permutation size, which results in efforts. As such, we also provide a multi-threaded implementa- computation times an order of magnitude shorter than CEGO, tion using up to 10 shared-memory CPU-cores. Using a range making it specially useful when both computation time and of large-scale random NK-landscapes with up to 10^6 variables, number of evaluations are limited. we provide evidence on the efficiency and effectiveness of the proposed hill climber and we highlight the strength of our par- On the Design and Anytime Performance of allel design in attaining substantial acceleration factors for the Indicator-based Branch and Bound for Multi-objective largest experimented functions. Combinatorial Optimization Alexandre Jesus, University of Coimbra, Univ. Lille, Luís Pa- Genetic Algorithm Niching by (Quasi-)Infinite Memory quete, University of Coimbra, Bilel Derbel, Univ. Lille, Inria Adrian Worring, TU Darmstadt, Benjamin Mayer, TU Darm- Lille - Nord Europe, Arnaud Liefooghe, Univ. Lille, Inria Lille - stadt, Kay Hamacher, TU Darmstadt Nord Europe In this article, we propose an indicator-based branch and Genetic Algorithms (GA) explore the search space of an objec- bound (I-BB) approach for multi-objective combinatorial op- tive function via mutation and recombination. Crucial for the timization that uses a best-first search strategy. In particular, search dynamics is the maintenance of diversity in the pop- assuming maximizing objectives, the next node to be processed ulation. Inspired by tabu search we add a mechanism to an is chosen with respect to the quality of its upper bound. This elitist GA’s selection step to ensure diversification by excluding quality is given by a binary quality indicator, such as the binary already visited areas of the search space. To this end, we use hypervolume or the ε-indicator, with respect to the archive Bloom filters as a probabilistic data structure to store a (quasi- of solutions maintained by the branch and bound algorithm. )infinite history. We discuss how this approach fits into the Although the I-BB will eventually identify the efficient set, we niching idea for GAs, finds an analogy in generational corre- are particularly interested in analyzing its anytime behavior as lation via epi-genetics in biology, and how the approach can a heuristic. Our experimental results, conducted on a multi- be regarded as a co-evolutionary edge case of previous niching objective knapsack problem with 2, 3, 5, and 7 objectives, indi- techniques. Furthermore, we apply this new technique to an cate that the I-BB can often outperform the naive depth-first NP-hard combinatorial optimization problem (ground states and breadth-first search strategies, both in terms of runtime of Ising spin glasses). We find by large-scale hyperparameter and anytime performance. The improvement is especially sig- scans that our elitist GA with quasi-infinite memory consis- nificant when the branching order for the decision variables is tently outperforms its respective standard GA variant without random, which suggests that the I-BB is particularly relevant a Bloom filter. when more favorable (problem-dependent) branching orders are not available. Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems ECOM 3 Ekhiñe Irurozki, Telecom Paris, Manuel López-Ibáñez, Univer- sity of Málaga Tuesday, July 13, 14:20-15:40 Room ECOM Expensive black-box combinatorial optimization problems Generating Hard Inventory Routing Problem Instances arise in practice when the objective function is evaluated by using Evolutionary Algorithms means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, Krzysztof Michalak, Wroclaw University of Economics the number of possible evaluations is typically several orders This paper studies a method of generating hard instances of of magnitude smaller than in non-expensive problems. Clas- the Inventory Routing Problem (IRP) using evolutionary algo- sical optimization methods are not useful in this scenario. In rithms. The difficulty of the generated instances is measured this paper, we propose and analyze UMM, an estimation-of- by the time used by the CPLEX solver to solve a given instance. distribution (EDA) algorithm based on a Mallows probabilistic In order to ensure that feasible problem instances are gener- model and unbalanced rank aggregation (uBorda). Experimen- ated, the Infeasibility Driven Evolutionary Algorithm (IDEA) tal results on black-box versions of LOP and PFSP show that is used along with seeding of the population with perturbed UMM outperforms the solutions obtained by CEGO, a Bayesian copies of an instance taken from a wellknown set of benchmark optimization algorithm for combinatorial optimization. Nev- instances. In the experiments a significant increase in prob- ertheless, a slight modification to CEGO, based on the differ- lem solving time was observed with respect to IRP instances ent interpretations for rankings and orderings, significantly proposed in the literature. Generated IRP instances do not improves its performance, thus producing solutions that are show any unusual properties that would make them unrealistic slightly better than those of UMM and dramatically better than and they are made available as benchmarks for optimization 112 Abstracts

algorithms. In the paper an attempt was made to compare gen- Iterative Partial Transcription (IPT) is an important recombi- erated problem instances and to draw conclusions regarding nation operator for Traveling Salesman Problem (TSP), and the structure of these instances, however, this has proven to be is a key component in the LKH inexact solver for the TSP.IPT a difficult task. shares many characteristics with Partition Crossover for the TSP. However, the standard implementation of IPT searches for all Local Search Pivoting Rules and the Landscape Global common subchains of sizes from 4 to n/2 between two parent Structure tours and thus has a time complexity of O(n2) in the worst case. Sara Tari, Université du Littoral Côte d’Opale, Gabriela Ochoa, In this paper, an efficient implementation of IPT is proposed University of Stirling that uses a special data structure called the Extended Edge Ta- In local search algorithms, the pivoting rule determines which ble (EET) to find the recombination components. We prove neighboring solution to select and thus strongly influences that the proposed technique is approximately equivalent to IPT the behavior of the algorithm and its capacity to sample good- and has a time complexity of O(n). The performance of the two quality local optima. The classical pivoting rules are first and implementations of IPT is compared based on some random best improvement, with alternative rules such as worst im- and benchmark TSP instances to establish the efficiency of the provement and maximum expansion recently studied on hill- proposed algorithm. climbing algorithms. This article conducts a thorough empir- ical comparison of five pivoting rules (best, first, worst, ap- The tiebreaking space of constructive heuristics for the proximated worst and maximum expansion) on two bench- permutation flowshop minimizing makespan mark combinatorial problems, NK landscapes and the uncon- strained binary quadratic problem (UBQP), with varied sizes Marcus Ritt, Universidade Federal do Rio Grande do Sul, and ruggedness. We present both a performance analysis of Alexander Benavides, Universidad Nacional de San Agustin the alternative pivoting rules within an iterated local search de Arequipa (ILS) framework and a fitness landscape analysis and visual- There has been intensive research on tiebreakers for insertion- ization using local optima networks. Our results reveal that based constructive heuristics in the permutation flow shop the performance of the pivoting rules within an ILS framework scheduling problem when the objective is to minimize the may differ from their performance as single climbers and that makespan. In this paper we analyze the space of all possi- worst improvement and maximum expansion can outperform ble tie-breaking rules for the most common insertion orders, classical pivoting rules. and evaluate the efficacy of existing tiebreakers based on this analysis. We find that an optimal tie breaker would produce An Efficient Implementation of Iterative Partial results that are about 1% better than current best tie breakers. Transcription for the Traveling Salesman Problem We propose a constructive heuristic based on a truncated cyclic Anirban Mukhopadhyay, University of Kalyani, Darrell Whit- best-first search in the space of all tie breakers and show that ley, Colorado State University, Renato Tinós, University of São it closely approximates the solutions that such an optimal tie Paulo breaker can achieve.

Evolutionary Machine Learning

EML 1 + GECH 3 + NE 2 - BP in four directions: North, South, West, and East, also plays a role, by communicating adaptations that are both new and Tuesday, July 13, 12:20-13:40 Rooms GECH, EML, NE fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner, Signal Propagation in a Gradient-Based and Evolutionary except that it uses a different spatial topology, i.e. a ring. Our central question is whether the different directionality of signal Learning System propagation (effectively migration to one or more neighbors Jamal Toutouh, Massachusetts Institute of Technology, Univer- on each side of a cell) meets or exceeds the performance qual- sity of Málaga, Una-May O’Reilly, Massachusetts Institute of ity and training efficiency of Lipizzaner Experimental analysis Technology on different datasets (i.e, MNIST, CelebA, and COVID-19 chest Generative adversarial networks (GANs) exhibit training X-ray images) shows that there are no significant differences pathologies that can lead to convergence-related degenerative between the performances of the trained generative models behaviors, whereas spatially-distributed, coevolutionary algo- by both methods. However, Lipi-Ring significantly reduces the rithms (CEAs) for GAN training, e.g. Lipizzaner, are empirically computational time (14.2%. . . 41.2%). Thus, Lipi-Ring offers robust to them. The robustness arises from diversity that oc- an alternative to Lipizzaner when the computational cost of curs by training populations of generators and discriminators training matters. in each cell of a toroidal grid. Communication, where signals in the form of parameters of the best GAN in a cell propagate Abstracts 113

Genetic Adversarial Training of Decision Trees In Multi-label (ML) classification, each instance is associated Francesco Ranzato, Dipartimento di Matematica, University of with more than one class label. Incorporating the label corre- Padova, Marco Zanella, Dipartimento di Matematica, Univer- lations into the model is one of the increasingly studied areas sity of Padova in the last decade, mostly due to its potential in training more accurate predictive models and dealing with noisy/missing la- We put forward a novel learning methodology for ensembles bels. Previously, multi-label learning classifier systems have of decision trees based on a genetic algorithm that is able to been proposed that incorporate the high-order label correla- train a decision tree for maximizing both its accuracy and its ro- tions into the model through the label powerset (LP) technique. bustness to adversarial perturbations. This learning algorithm However, such a strategy cannot take advantage of the valuable internally leverages a complete formal verification technique statistical information in the label space to make more accu- for robustness properties of decision trees based on abstract rate inferences. Such information becomes even more crucial interpretation, a well-known static program analysis technique. in ML image classification problems where the number of la- We implemented this genetic adversarial training algorithm in bels can be very large. In this paper, we propose a multi-label a tool called MetaSilvae and we experimentally evaluated it on learning classifier system that leverages a structured represen- some standard reference datasets used in adversarial training. tation for the labels through undirected graphs to utilize the The experimental results show that MetaSilvae is able to train label similarities when evolving rules. More specifically, we pro- robust models that compete with and often improve on the pose a novel scheme for covering classifier actions, as well as current state-of-the-art of adversarial training of decision trees a method to calculate ML prediction arrays. The effectiveness while being much more compact and therefore interpretable of this method is demonstrated by experimenting on multiple and efficient tree models. benchmark datasets and comparing the results with multiple well-known ML classification algorithms.

EML 2 A Systematic Comparison Study on Hyperparameter Tuesday, July 13, 14:20-15:40 Room EML Optimisation of Graph Neural Networks for Molecular Property Prediction Optimizing Loss Functions Through Multi-Variate Taylor Yingfang Yuan, Heriot-Watt University, Wenjun Wang, Heriot- Polynomial Parameterization Watt University, Wei Pang, Heriot-Watt University Santiago Gonzalez, University of Texas at Austin, Cognizant AI Graph neural networks (GNNs) have been proposed for a wide Labs, Risto Miikkulainen, University of Texas at Austin, Cog- range of graph-related learning tasks. In particular, in recent nizant AI Labs years, an increasing number of GNN systems were applied to Metalearning of deep neural network (DNN) architectures and predict molecular properties. However, a direct impediment is hyperparameters has become an increasingly important area to select appropriate hyperparameters to achieve satisfactory of research. Loss functions are a type of metaknowledge that is performance with lower computational cost. Meanwhile, many crucial to effective training of DNNs, however, their potential molecular datasets are far smaller than many other datasets role in metalearning has not yet been fully explored. Whereas in typical deep learning applications. Most hyperparameter early work focused on genetic programming (GP) on tree repre- optimization (HPO) methods have not been explored in terms sentations, this paper proposes continuous CMA-ES optimiza- of their efficiencies on such small datasets in the molecular tion of multivariate Taylor polynomial parameterizations. This domain. In this paper, we conducted a theoretical analysis of approach, TaylorGLO, makes it possible to represent and search common and specific features for two state-of-the-art and pop- useful loss functions more effectively. In MNIST, CIFAR-10, and ular algorithms for HPO: TPE and CMA-ES, and we compared SVHN benchmark tasks, TaylorGLO finds new loss functions them with random search (RS), which is used as a baseline. that outperform the standard cross-entropy loss as well as novel Experimental studies are carried out on several benchmarks loss functions previously discovered through GP,in fewer gen- in MoleculeNet, from different perspectives to investigate the erations. These functions serve to regularize the learning task impact of RS, TPE, and CMA-ES on HPO of GNNs for molec- by discouraging overfitting to the labels, which is particularly ular property prediction. In our experiments, we concluded useful in tasks where limited training data is available. The that RS, TPE, and CMA-ES have their individual advantages results thus demonstrate that loss function optimization is a in tackling different specific molecular problems. Finally, we productive new avenue for metalearning. believe our work will motivate further research on GNN as ap- plied to molecular machine learning problems in chemistry An Effective Action Covering for Multi-label Learning and materials sciences. Classifier Systems: A Graph-Theoretic Approach Shabnam Nazmi, North Carolina A&T State University, Ab- Regularized Evolutionary Population-Based Training dollah Homaifar, North Carolina A&T State University, Mohd Jason Liang, Cognizant, Santiago Gonzalez, Cognizant, The Anwar, North Carolina A&T State University University of Texas at Austin, Hormoz Shahrzad, Cognizant, 114 Abstracts

Risto Miikkulainen, Cognizant, The University of Texas at real-life datasets. Notably, all the evaluated CVIs performed Austin poorly in clustering the non-linearly separable data because of Metalearning of deep neural network (DNN) architectures and the assumptions about data distributions. hyperparameters has become an increasingly important area of research. At the same time, network regularization has been Genetic Programming for Borderline Instance Detection in recognized as a crucial dimension to effective training of DNNs. High-dimensional Unbalanced Classification However, the role of metalearning in establishing effective reg- Wenbin Pei, Victoria University of Wellington, Bing Xue, Vic- ularization has not yet been fully explored. There is recent toria University of Wellington, Lin Shang, Nanjing University, evidence that loss-function optimization could play this role, Mengjie Zhang, Victoria University of Wellington however it is computationally impractical as an outer loop to full training. This paper presents an algorithm called Evo- In classification, when class overlap is intertwined with the is- lutionary Population-Based Training (EPBT) that interleaves sue of class imbalance, it is often challenging to discover useful the training of a DNN’s weights with the metalearning of loss patterns because of an ambiguous boundary between the ma- functions. They are parameterized using multivariate Taylor jority class and the minority class. This becomes more difficult expansions that EPBT can directly optimize. Such simultane- if the data is high-dimensional. To date, very few pieces of work ous adaptation of weights and loss functions can be deceptive, have investigated how the class overlap issue can be effectively and therefore EPBT uses a quality-diversity heuristic called addressed or alleviated in classification with high-dimensional Novelty Pulsation as well as knowledge distillation to prevent unbalanced data. In this paper, we propose a new genetic pro- overfitting during training. On the CIFAR-10 and SVHN image gramming based method, which is able to automatically and classification benchmarks, EPBT results in faster, more accu- directly detect borderline instances, in order to address the rate learning. The discovered hyperparameters adapt to the class overlap issue in classification with high-dimensional un- training process and serve to regularize the learning task by balanced data. In the proposed method, each individual has discouraging overfitting to the labels. EPBT thus demonstrates two trees to be trained together based on different classification a practical instantiation of regularization metalearning based rules. The proposed method is examined and compared with on simultaneous training. baseline methods on high-dimensional unbalanced datasets. Experimental results show that the proposed method achieves better classification performance than the baseline methods in EML 3 almost all cases. Wednesday, July 14, 10:20-11:40 Room EML Convergence Analysis of Rule-Generality on the XCS A survey of cluster validity indices for automatic data Classifier System clustering using differential evolution Yoshiki Nakamura, Yokohama National University, Motoki Ho- Adán José-García, University of Lille, CINVESTAV-IPN, Wilfrido riuchi, Yokohama National University, Masaya Nakata, Yoko- Gómez-Flores, Cinvestav-IPN hama National University In cluster analysis, the automatic clustering problem refers to The XCS classifier system adaptively controls a rule-generality the determination of both the appropriate number of clusters of a rule-condition through a rule-discovery process. How- and the corresponding natural partitioning. This can be ad- ever, there is no proof that the rule-generality can eventually dressed as an optimization problem in which a cluster validity converge to its optimum value even under some ideal assump- index (CVI) is used as a fitness function to evaluate the qual- tions. This paper conducts a convergence analysis of the rule- ity of potential solutions. Different CVIs have been proposed generality on the rule- discovery process with the ternary al- in the literature, aiming to identify adequate cluster solutions phabet coding. Our analysis provides the first proof that an in terms of intracluster cohesion and intercluster separation. average rule-generality of rules in a population can converge to However, it is important to identify the scenarios in which these its optimum value under some assumptions. This proof can be CVIs perform well and their limitations. This paper evaluates used to mathematically conclude that the XCS framework has a the effectiveness of 22 different CVIs used as fitness functions natural pressure to explore rules toward optimum rules if XCS in an evolutionary clustering algorithm named ACDE based satisfies our derived conditions. In addition, our theoretical on differential evolution. Several synthetic datasets are con- result returns a rough setting-up guideline for the maximum sidered: linearly separable data having both well-separated population size, the mutation rate, and the GA threshold, im- and overlapped clusters, and non-linearly separable data hav- proving the convergence speed of the rule-generality and the ing arbitrarily-shaped clusters. Besides, real-life datasets are XCS performance. also considered. The experimental results indicate that the Silhouette index consistently reached an acceptable perfor- mance in linearly separable data. Furthermore, the indices Coevolution of Remaining Useful Lifetime Estimation Calinski-Harabasz, Davies-Bouldin, and generalized Dunn ob- Pipelines for Automated Predictive Maintenance tained an adequate clustering performance in synthetic and Tanja Tornede, University of Paderborn, Alexander Tornede, Abstracts 115

University of Paderborn, Marcel Wever, University of Pader- to the need for methods generating feature representations, born, Eyke Hüllermeier, University of Munich the search space of candidate pipelines enlarges. Moreover, the runtime of a single pipeline increases substantially. In this pa- Automated machine learning (AutoML) strives for automati- per, we tackle these problems by partitioning the search space cally constructing and configuring compositions of machine into two sub-spaces, one for feature extraction methods and learning algorithms, called pipelines, with the goal to optimize one for regression methods, and employ cooperative coevolu- a suitable performance measure on a concrete learning task. tion for searching a good combination. Thereby, we benefit So far, most AutoML tools are focused on standard problem from the fact that the generated feature representations can classes, such as classification and regression. In the field of be cached, whence the evaluation of multiple regressors based predictive maintenance, especially the estimation of remaining on the same feature representation speeds up, allowing the useful lifetime (RUL), the task of AutoML becomes more com- evaluation of more candidate pipelines. Experimentally, we plex. In particular, a good feature representation for multivari- show that our coevolutionary strategy performs superior to the ate sensor data is essential to achieve good performance. Due baselines.

Evolutionary Multiobjective Optimization

EMO 1 We propose a new algorithm for the extreme hypervolume con- tributor/contribution problem, i.e. the problem of finding the Tuesday, July 13, 12:20-13:40 Room EMO point with minimum/maximum contribution to the hypervol- Environmental Selection Using a Fuzzy Classifier for ume and (optionally) the value of this extreme contribution. Multiobjective Evolutionary Algorithms Our algorithm is motivated by the Improved Quick Hypervol- ume (IQHV) which works in a divide and conquer manner, Jinyuan Zhang, Southern University of Science and Technology, and uses lower and upper bounds of the contribution to speed Hisao Ishibuchi, Southern University of Science and Technol- up calculations. It may be applied directly in multiobjective ogy, Ke Shang, Southern University of Science and Technology, optimization methods that select solutions based on the ex- Linjun He, Southern University of Science and Technology, Lie treme contribution to the hypervolume, or within the greedy Meng Pang, Southern University of Science and Technology, algorithms for hypervolume subset selection problem often Yiming Peng, Southern University of Science and Technology used in EMO. The proposed algorithm is the first dedicated The quality of solutions in multiobjective evolutionary al- algorithm for the maximum contribution problem and in the gorithms (MOEAs) is usually evaluated by objective func- reported computational experiment it outperforms on most tions. However, function evaluations (FEs) are usually time- data sets state-of-the-art IWFG algorithm for the minimum consuming in real-world problems. A large number of FEs limit contribution problem. We show that the proposed algorithm the application of MOEAs. In this paper, we propose a fuzzy uses the lower and upper bounds very well by comparing it to classifier-based selection strategy to reduce the number of FEs a reference algorithm having an access to an oracle providing of MOEAs. First, all evaluated solutions in previous generations the best contributor in advance. We propose also a modifi- are used to build a fuzzy classifier. Second, the built fuzzy clas- cation of the general IQHV algorithm scheme in which the sifier is used to predict each unevaluated solution’s label and its order of objectives processing which influences construction membership degree. The reproduction procedure is repeated of sub-problems is heuristically adapted and show that this to generate enough offspring solutions (classified as positive by modification improves the efficiency of the algorithm. the classifier). Next, unevaluated solutions are sorted based on their membership degrees in descending order. The same num- ber of solutions as the population size are selected from the top Distance-based Subset Selection Revisited of the sorted unevaluated solutions. Then, the best half of the Ke Shang, Southern University of Science and Technology, chosen solutions are selected and stored in the new population Hisao Ishibuchi, Southern University of Science and Technol- without evaluations. The other half solutions are evaluated. Fi- ogy, Yang Nan, Southern University of Science and Technology nally, the evaluated solutions are used together with evaluated In this paper, we revisit the distance-based subset selection current solutions for environmental selection to form another (DSS) algorithm in evolutionary multi-objective optimization. half of the new population. The proposed strategy is integrated First, we show one drawback of the DSS algorithm, i.e., a uni- into two MOEAs. Our experimental results demonstrate the formly distributed solution set cannot always be selected. Then, effectiveness of the proposed strategy on reducing FEs. we show that this drawback can be overcome by maximizing the uniformity level of the selected solution set, which is de- Quick Extreme Hypervolume Contribution Algorithm fined by the minimum distance between two solutions in the Andrzej Jaszkiewicz, Poznan University of Technology, Piotr solution set. Furthermore, we prove that the DSS algorithm is a Zielniewicz, Poznan University of Technology greedy inclusion algorithm with respect to the maximization of 116 Abstracts

the uniformity level. Based on this conclusion, we generalize binatorial optimisation, to a benchmark set of 1200 randomly- DSS as a subset selection problem where the objective is to generated multiobjective interpolated continuous optimisa- maximize the uniformity level of the subset. In addition to the tion problems (MO-ICOPs). We also explore the benefits of greedy inclusion DSS algorithm, a greedy removal algorithm evaluating the considered landscape features on the basis of a and an iterative algorithm are proposed for the generalized fixed-size sampling of the search space. This allows fine control DSS problem. We also extend the Euclidean distance in the over cost when aiming for an efficient application of feature- original DSS to other widely-used and user-defined distances. based automated performance prediction and algorithm selec- We conduct extensive experiments on solution sets over differ- tion. While previous work shows that the parameters used to ent types of Pareto fronts to compare the three DSS algorithms generate MO-ICOPs are able to discriminate the convergence with different distances. Our results suggest the usefulness of behaviour of four state-of-the-art multi-objective evolutionary the generalized DSS for selecting a uniform subset. The ef- algorithms, our experiments reveal that the proposed (black- fect of using different distances on the selected subsets is also box) landscape features used as predictors deliver a similar analyzed. accuracy when combined with a classification model. In ad- dition, we analyse the relative importance of each feature for Bayesian Preference Learning for Interactive performance prediction and algorithm selection. Multi-objective Optimisation Kendall Taylor, RMIT University, Huong Ha, RMIT University, Pareto Compliance from a Practical Point of View Minyi Li, RMIT University, Jeffrey Chan, RMIT University, Xi- aodong Li, RMIT University Jesús Falcón-Cardona, Universidad Autónoma Metropolitana Unidad Cuajimalpa, Saúl Zapotecas-Martínez, Universidad This work proposes a Bayesian optimisation with Gaussian Pro- Autónoma Metropolitana Unidad Cuajimalpa, Abel García- cess approach to learn decision maker (DM) preferences in Nájera, Universidad Autónoma Metropolitana Unidad Cua- the attribute search space of a multi-objective optimisation jimalpa problem (MOP). The DM is consulted periodically during opti- misation of the problem and asked to provide their preference Pareto compliance is a critical property of quality indicators over a series of pairwise comparisons of candidate solutions. (QIs) focused on measuring convergence to the Pareto front. After each consultation, the most preferred solution is used This property allows a QI not to contradict the order imposed as the reference point in an appropriate multiobjective opti- by the Pareto dominance relation. Hence, Pareto-compliant misation evolutionary algorithm (MOEA). The rationale for QIs are remarkable when comparing approximation sets of using Bayesian optimisation is to identify the most preferred multi-objective evolutionary algorithms (MOEAs) since they location in the decision search space with the least number do not produce misleading conclusions. However, the practi- of DM queries, thereby minimising DM cognitive burden and cal effect of Pareto-compliant QIs as the backbone of MOEAs’ fatigue. This enables non-expert DMs to be involved in the survival mechanisms is not entirely understood. In this paper, optimisation process and make more informed decisions. We we study the practical effect of IGD++ (which is a combina- further reduce the number of preference queries required, by tion of IGD+ and the hypervolume indicator), IGD+, and IGD, progressively redefining the Bayesian search space to reflect which are Pareto-compliant, weakly Pareto-compliant, and not the MOEA’s decision bounds as it converges toward the Pareto Pareto-compliant QIs, respectively. To this aim, we analyze the Front. We demonstrate how this approach can locate a ref- convergence and diversity properties of steady-state MOEAs erence point close to an unknown preferred location on the based on the previously mentioned QIs throughout the whole Pareto Front, of both benchmark and real-world problems with evolutionary process. Our experimental results showed that, relatively few pairwise comparisons. in general, the practical effect of a Pareto-compliant QI in a selection mechanism is not very significant concerning weaker QIs, taking into account the whole evolutionary process. EMO 2 - BP Tuesday, July 13, 14:20-15:40 Room EMO Greedy Approximated Hypervolume Subset Selection for Landscape Features and Automated Algorithm Selection for Many-objective Optimization Multi-objective Interpolated Continuous Optimisation Ke Shang, Southern University of Science and Technology, Problems Hisao Ishibuchi, Southern University of Science and Technol- Arnaud Liefooghe, Univ. Lille, Inria Lille - Nord Europe, ogy, Weiyu Chen, Southern University of Science and Technol- Sébastien Verel, Université du Littoral Côte d’Opale, Ben- ogy jamin Lacroix, Robert Gordon University, Alexandru-Ciprian Hypervolume subset selection (HSS) aims to select a subset Z˘avoianu, Robert Gordon University, John McCall, Robert Gor- from a candidate solution set so that the hypervolume of the don University selected subset is maximized. Due to its NP-hardness nature, In this paper, we demonstrate the application of features from the greedy algorithms are the most efficient for solving HSS in landscape analysis, initially proposed for multi-objective com- many-objective optimization. However, when the number of Abstracts 117

objectives is large, the calculation of the hypervolume contri- criteria decision aiding. When it comes to the incorporated evo- bution in the greedy HSS is time-consuming, which makes the lutionary framework, iMOEA-HA implements a steady-state, greedy HSS inefficient. To solve this issue, in this paper we pro- quasi decomposition-based model with a restricted mating pose a greedy approximated HSS algorithm. The main idea is pool. To drive the population towards the Decision Maker’s to use an R2-based hypervolume contribution approximation (DM’s) preferred region in the Pareto front, iMOEA-HA exploits method in the greedy HSS. In the algorithm implementation, a the DM’s pairwise comparisons of solutions from the current utility tensor structure is introduced to facilitate the calculation population and assesses all solutions as imposed by their holis- of the hypervolume contribution approximation. In addition, tic acceptability indices. These acceptabilities are built on the the tensor information in the last step is utilized in the current probabilities of attaining a certain number of the most pre- step to accelerate the calculation. We also apply the lazy strat- ferred ranks. We evaluated the proposed algorithm’s perfor- egy in the proposed algorithm to further improve its efficiency. mance on a vast set of optimization problems with different We test the greedy approximated HSS algorithm on 3-10 ob- properties and numbers of objectives. Moreover, we compared jective candidate solution sets. The experimental results show iMOEA-HA with existing interactive evolutionary algorithms that the proposed algorithm is much faster than the state-of- that can be deemed its predecessors, proving its high effective- the-art greedy HSS algorithm in many-objective optimization ness and competitiveness. while their hypervolume performance is almost the same. Metric for Evaluating Normalization Methods in Multiobjective Optimization EMO 3 Linjun He, Southern University of Science and Technology, Na- Wednesday, July 14, 10:20-11:40 Room EMO tional University of Singapore, Hisao Ishibuchi, Southern Uni- versity of Science and Technology, Dipti Srinivasan, National Hypervolume in Biobjective Optimization Cannot Converge University of Singapore Faster Than Ω(1/p) Normalization is an important algorithmic component for mul- Eugénie Marescaux, École Polytechnique, Inria, Nikolaus tiobjective evolutionary algorithms (MOEAs). Different nor- Hansen, Inria malization methods have been proposed in the literature. Re- The hypervolume indicator is widely used by multi-objective cently, several studies have been conducted to examine the optimization algorithms and for assessing their performance. effects of normalization methods. However, the existing evalu- We investigate a set of p vectors in the biobjective space that ation methods for investigating the effects of normalization are maximizes the hypervolume indicator with respect to some limited due to their drawbacks. In this paper, we discuss the reference point, referred to as p-optimal distribution. We prove limitations of the existing evaluation methods. A new metric explicit lower and upper bounds on the gap between the hy- has been proposed to facilitate the investigation of normaliza- pervolumes of the p-optimal distribution and the -optimal tion methods. Our analysis clearly shows the superiority of ∞ distribution (the Pareto front) as a function of p, of the ref- the proposed metric over the existing methods. We also use erence point, and of some Lipschitz constants. On a wide the proposed metric to compare three popular normalization class of functions, this optimality gap can not be smaller than methods on problems with different Pareto front shapes. Ω(1/p), thereby establishing a bound on the optimal conver- gence speed of any algorithm. For functions with either bilips- Realistic Utility Functions Prove Difficult for chitz or convex Pareto fronts, we also establish an upper bound State-of-the-Art Interactive Multiobjective Optimization and the gap is hence Θ(1/p). The presented bounds are not Algorithms only asymptotic. In particular, functions with a linear Pareto Seyed Shavarani, University of Manchester, Manuel López- front have the normalized exact gap of 1/(p 1) for any refer- + Ibáñez, University of Manchester, Joshua Knowles, Invenia ence point dominating the nadir point. We empirically inves- Labs tigate on a small set of Pareto fronts the exact optimality gap for values of p up to 1000 and find in all cases a dependency Improvements to the design of interactive Evolutionary Multi- resembling 1/(p CONST). objective Algorithms (iEMOAs) are unlikely without quantita- + tive assessment of their behaviour in realistic settings. Experi- ments with human decision-makers (DMs) are of limited scope Interactive evolutionary multiple objective optimization due to the difficulty of isolating individual biases and replicat- algorithm using a fast calculation of holistic acceptabilities ing the experiment with enough subjects, and enough times, Michał Tomczyk, Poznan University of Technology, Miłosz to obtain confidence in the results. Simulation studies may Kadzi´nski, Poznan University of Technology help to overcome these issues, but they require the use of realis- We propose a novel algorithm, called iMOEA-HA, for interac- tic simulations of decision-makers. Machine decision-makers tive evolutionary multiple objective optimization. It combines, (MDMs) provide a way to carry out such simulation studies, in an original way, the state-of-the-art paradigms postulated however, studies so far have relied on simple utility functions. in evolutionary multiple objective optimization and multiple In this paper, we analyse and compare two state-of-the-art 118 Abstracts

iEMOAs by means of a MDM that uses a sigmoid-shaped utility three objectives, the two iEMOAs do not consistently recover function. This sigmoid utility function is based on psycho- the most-preferred points. We hope that these findings pro- logically realistic models from behavioural economics, and vide an impetus for more directed design and analysis of future replicates several realistic human behaviours. Our findings iEMOAs. are that, on a variety of well-known benchmarks with two and

Evolutionary Numerical Optimization

ACO-SI 3 + ENUM 1 + THEORY 1 - BP They are often tightly interconnected, which causes difficul- ties in understanding the impact of each component on the Tuesday, July 13, 14:20-15:40 Rooms ACO-SI, ENUM, algorithm performance. To contribute to such understand- THEORY ing, we analyse what constitutes the evolution control of three A Matrix Adaptation Evolution Strategy for Optimization on surrogate-assisted versions of the state-of-the-art algorithm for General Quadratic Manifolds continuous black-box optimization � the Covariance Matrix Adaptation Evolution Strategy. We implement and empirically Patrick Spettel, Vorarlberg University of Applied Sciences, compare all possible combinations of the regression models Hans-Georg Beyer, Vorarlberg University of Applied Sciences employed in those methods with the three evolution controls An evolution strategy design is presented that allows for an encountered in them. An experimental investigation of all evolution on general quadratic manifolds. That is, it covers those combinations allowed us to asses the influence of the elliptic, parabolic, and hyperbolic equality constraints. The models and their evolution control separately. The experiments peculiarity of the presented algorithm design is that it is an are performed on the noiseless and noisy benchmarks of the interior point method. It evaluates the objective function only Comparing-Continuous-Optimisers platform and a real-world for feasible search parameter vectors and it evolves itself on simulation benchmark, all in the expensive scenario, where the nonlinear constraint manifold. This is achieved by a closed only a small budget of evaluations is available. form transformation of an individual’s parameter vector, which is in contrast to iterative repair mechanisms. Results of differ- Explorative Data Analysis of Time Series based Algorithm ent experiments are presented. A test problem consisting of a Features of CMA-ES Variants spherical objective function and a single hyperbolic/parabolic Jacob de Nobel, Leiden University, Hao Wang, Leiden Univer- equality constraint is used. It is designed to be scalable in the sity, Thomas Baeck, Leiden University dimension and it is used to compare the performance of the In this study, we analyze behaviours of the well-known CMA-ES developed algorithm with other optimization methods sup- by extracting the time-series features on its dynamic strategy porting constraints. The experiments show the effectiveness of parameters. An extensive experiment was conducted on twelve the proposed algorithm on the considered problems. CMA-ES variants and 24 test problems taken from the BBOB (Black-Box Optimization Bench-marking) testbed, where we ENUM 2 used two different cutoff times to stop those variants. We uti- lized the tsfresh package for extracting the features and per- Wednesday, July 14, 10:20-11:40 Room ENUM formed the feature selection procedure using the Boruta algo- rithm, resulting in 32 features to distinguish either CMA-ES Interaction between Model and Its Evolution Control in variants or the problems. After measuring the number of prede- Surrogate-Assisted CMA Evolution Strategy fined targets reached by those variants, we contrive to predict ZbynˇekPitra, Faculty of Nuclear Sciences and Physical Engi- those measured values on each test problem using the feature. neering of the Czech Technical University; Institute of Com- From our analysis, we saw that the features can classify the puter Science, Academy of Sciences of the Czech Republic, CMA-ES variants, or the function groups decently, and show a Marek Hanuš, Czech Technical University, Jan Koza, Czech potential for predicting the performance of those variants. We Technical University, Academy of Sciences of the Czech Repub- conducted a hierarchical clustering analysis on the test prob- lic, JiˇríTumpach, Charles University, Academy of Sciences of lems and noticed a drastic change in the clustering outcome the Czech Republic, Martin Holeˇna, Academy of Sciences of when comparing the longer cutoff time to the shorter one, indi- the Czech Republic cating a huge change in search behaviour of the algorithm. In Surrogate regression models have been shown as a valuable general, we found that with longer time series, the predictive technique in evolutionary optimization to save evaluations power of the time series features increase. of expensive black-box objective functions. Each surrogate modelling method has two complementary components: the Saddle Point Optimization with Approximate Minimization employed model and the control of when to evaluate the model Oracle and when the true objective function, aka evolution control. Youhei Akimoto, University of Tsukuba, RIKEN AIP Abstracts 119

A major approach to saddle point optimization University min max f (x, y) is a gradient based approach as is popular- x y Differential MAP-Elites is a novel algorithm that combines the ized by generative adversarial networks (GANs). In contrast, we illumination capacity of CVT-MAP-Elites with the continuous- analyze an alternative approach relying only on an oracle that space optimization capacity of Differential Evolution. The al- solves a minimization problem approximately. Our approach gorithm is motivated by observations that illumination algo- locates approximate solutions x and y to min f (x , y) and 0 0 x0 0 rithms, and quality-diversity algorithms in general, offer quali- max f (x, y ) at a given point (x, y) and updates (x, y) toward y0 0 tatively new capabilities and applications for evolutionary com- these approximate solutions (x0, y0) with a learning rate η. On putation yet are in their original versions relatively unsophisti- locally strong convex–concave smooth functions, we derive cated optimizers. The basic Differential MAP-Elites algorithm, conditions on η to exhibit linear convergence to a local saddle introduced for the first time here, is relatively simple in that point, which reveals a possible shortcoming of recently devel- it simply combines the operators from Differential Evolution oped robust adversarial reinforcement learning algorithms. We with the map structure of CVT-MAP-Elites. Experiments based develop a heuristic approach to adapt η derivative-free and on 25 numerical optimization problems suggest that Differen- implement zero-order and first-order minimization algorithms. tial MAP-Elites clearly outperforms CVT-MAP-Elites, finding Numerical experiments are conducted to show the tightness better-quality and more diverse solutions. of the theoretical results as well as the usefulness of the η adaptation mechanism. Towards Exploratory Landscape Analysis for Large-scale Augmented Lagrangian, penalty techniques and surrogate Optimization: A Dimensionality Reduction Framework modeling for constrained optimization with CMA-ES Ryoji Tanabe, Yokohama National University, Riken AIP Paul Dufossé, Inria, Thales DMS, Nikolaus Hansen, Inria In this paper, we investigate a non-elitist Evolution Strategy Although exploratory landscape analysis (ELA) has shown its designed to handle black-box constraints by an adaptive Aug- effectiveness in various applications, most previous studies mented Lagrangian penalty approach, AL-(µ/µ_w , λ)-CMA-ES, focused only on low- and moderate-dimensional problems. on problems with up to 28 constraints. Based on stability and Thus, little is known about the scalability of the ELA approach performance observations, we propose an improved default for large-scale optimization. In this context, first, this paper an- parameter setting. We exhibit failure cases of the Augmented alyzes the computational cost of features in the flacco package. Lagrangian technique and show how surrogate modeling of the Our results reveal that two important feature classes (ela_level constraints can overcome some difficulties. Several variants of and ela_meta) cannot be applied to large-scale optimization AL-CMA-ES are compared on a set of nonlinear constrained due to their high computational cost. To improve the scalabil- problems from the literature. Simple adaptive penalty tech- ity of the ELA approach, this paper proposes a dimensional- niques serve as a baseline for comparison. ity reduction framework that computes features in a reduced lower-dimensional space than the original solution space. We demonstrate that the proposed framework can drastically re- ENUM 3 duce the computation time of ela_level and ela_meta for large Wednesday, July 14, 12:20-13:40 Room ENUM dimensions. In addition, the proposed framework can make the cell-mapping feature classes scalable for large-scale opti- Self-Referential Quality Diversity Through Differential Map- mization. Our results also show that features computed by the Elites proposed framework are beneficial for predicting the high-level Tae Jong Choi, Kyungil University, Julian Togelius, New York properties of the 24 large-scale BBOB functions.

Genetic Algorithms

GA 1 used in the literature to achieve this goal. Even though Sym- bolic Regression is sometimes associated with the potential Monday, July 12, 14:20-15:40 Room GA of generating interpretable expressions, there is no guaran- tee that the returned function will not contain complicated Simulated Annealing for Symbolic Regression constructs or even bloat. The Interaction-Transformation (IT) Daniel Kantor, Unicamp, Fernando Von Zuben, Unicamp, representation was recently proposed to alleviate this issue Fabricio de Franca, UFABC by constraining the search space to expressions following a Symbolic regression aims to hypothesize a functional relation- simple and comprehensive pattern. In this paper, we resort ship involving explanatory variables and one or more depen- to Simulated Annealing to search for a symbolic expression dent variables, based on examples of the desired input-output using the IT representation. Simulated Annealing exhibits an behavior. Genetic programming is a meta-heuristic commonly intrinsic ability to escape from poor local minima, which is 120 Abstracts

demonstrated here to yield competitive results, particularly in perform efficiently on different instance types. However, find- terms of generalization, when compared with state-of-the-art ing the best solver for all instances is challenging. A hybrid Symbolic Regression techniques, that depend on population- of the Mixing Genetic Algorithm (MGA) and Edge Assembly based meta-heuristics, and committees of learning machines. Crossover (EAX) has been shown to perform well on hard in- stances. The MGA uses Generalized Partition Crossover (GPX) Partition Crossover for Continuous Optimization: ePX to find the best and worst out of 2k possible solutions, where Renato Tinós, University of São Paulo, Darrell Whitley, Col- k is a decomposition factor of two-parent tours. MGA mixes orado State University, Francisco Chicano, University of the edges without any loss of diversity in the population. The Málaga, Gabriela Ochoa, University of Stirling best individuals move to the top of the population. The worst individuals are filtered to the bottom of the population. Previ- Partition crossover (PX) is an efficient recombination operator ously, MGA was applied to TSP instances with less than 4,500 for gray-box optimization. PX is applied in problems where vertices. In this article, various Island Model implementations the objective function can be written as a sum of subfunctions of MGA are introduced to handle larger problem sizes. The f (.). In PX, the variable interaction graph (VIG) is decomposed l island model uses two mixing policies - migration, which does by removing vertices with common variables. Parent variables not lose diversity, and replacement, which loses some popu- are inherited together during recombination if they are part of lation diversity. The islands are configured in two patterns - the same connected recombining component of the decom- a ring and a hypercube. An ensemble running multiple ver- posed VIG. A new way of generating the recombination graph is sions of an hybrid of MGA and EAX algorithms yields excellent proposed here. The VIG is decomposed by removing edges as- performance for problems as large as 85,900. sociated with subfunctions fl (.) that have similar evaluation for combinations of variables inherited from the parents. By doing so, the partial evaluations of fl (.) are taken into account when GA 2 - BP decomposing the VIG. This allows the use of partition crossover in continuous optimization. Results of experiments where lo- Tuesday, July 13, 12:20-13:40 Room GA cal optima are recombined indicate that more recombining Analysis of Evolutionary Diversity Optimisation for components are found. When the proposed epsilon-PX (ePX) Permutation Problems is compared with other recombination operators in Genetic Algorithms and Differential Evolution, better performance is Anh Do, The University of Adelaide, Mingyu Guo, The Univer- obtained when the epistasis degree is low. sity of Adelaide, Aneta Neumann, The University of Adelaide, Frank Neumann, The University of Adelaide Quadratization of Gray Coded Representations, Long Path Generating diverse populations of high quality solutions has Problems and Needle Functions gained interest as a promising extension to the traditional op- Darrell Whitley, Colorado State University, Francisco Chicano, timization tasks. We contribute to this line of research by University of Malaga, Hernan Aquirre, Shinshu University studying evolutionary diversity optimization for two of the In Evolutionary Computation, it is informative to ask what hap- most prominent permutation problems, namely the Traveling pens when well known benchmarks and bit representations Salesperson Problem (TSP) and Quadratic Assignment Prob- are transformed into quadratic pseudo-Boolean optimization lem (QAP). We explore the worst-case performance of a simple problems. Such transforms are commonly used in quantum mutation-only evolutionary algorithm with different mutation computing in order to reduce nonlinearity to k-bounded in- operators, using an established diversity measure. Theoreti- teractions. First we show that Gray code representations are cal results show most mutation operators for both problems transformed into linear encodings with quadratic constraints. ensure production of maximally diverse populations of suffi- Next we look at Long Path problems which are constructed so ciently small size within cubic expected run-time. We perform that bit flip local search requires exponential time to converge experiments on QAPLIB instances in unconstrained and con- to a global or local optimum. We show that Long Path problems strained settings, and reveal much more optimistic practical are similar to reflected Gray codes in both construction and performances. Our results should serve as a baseline for future complexity. Finally, a basic form of the "Needle in a haystack" studies. problem is transformed into a problem that can be optimally solved in linear time. A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning A Parallel Ensemble Genetic Algorithm for the Traveling Arkadiy Dushatskiy, Centrum Wiskunde & Informatica, Tanja Salesman Problem Alderliesten, Leiden University Medical Center, Peter Bosman, Swetha Varadarajan, Colorado State University, Darrell Whitley, Centrum Wiskunde & Informatica, TU Delft Colorado State University We propose a novel surrogate-assisted Evolutionary Algorithm A parallel ensemble of Genetic Algorithms for the Traveling for solving expensive combinatorial optimization problems. Salesman Problem (TSP) is proposed. Different TSP solvers We integrate a surrogate model, which is used for fitness value Abstracts 121

estimation, into a state-of-the-art P3-like variant of the Gene- solution. Furthermore, they should differ in structure with re- Pool Optimal Mixing Algorithm (GOMEA) and adapt the re- spect to an entropy-based diversity measure. To this end we sulting algorithm for solving non-binary combinatorial prob- propose a simple (µ 1)-EA with initial approximate solutions + lems. We test the proposed algorithm on an ensemble learning calculated by a well-known FPTAS for the KP.We investigate the problem. Ensembling several models is a common Machine effect of different standard mutation operators and introduce Learning technique to achieve better performance. We con- biased mutation and crossover which puts strong probability sider ensembles of several models trained on disjoint subsets on flipping bits of low and/or high frequency within the popula- of a dataset. Finding the best dataset partitioning is naturally a tion. An experimental study on different instances and settings combinatorial non-binary optimization problem. Fitness func- shows that the proposed mutation operators in most cases per- tion evaluations can be extremely expensive if complex models, form slightly inferior in the long term, but show strong benefits such as Deep Neural Networks, are used as learners in an en- if the number of function evaluations is severely limited. semble. Therefore, the number of fitness function evaluations is typically limited, necessitating expensive optimization tech- niques. In our experiments we use five classification datasets GA 3 from the OpenML-CC18 benchmark and Support-vector Ma- Wednesday, July 14, 12:20-13:40 Room GA chines as learners in an ensemble. The proposed algorithm demonstrates better performance than alternative approaches, A Genetic Algorithm Approach for the Euclidean Steiner including Bayesian optimization algorithms. It manages to find Tree Problem with Soft Obstacles better solutions using just several thousand fitness function Manou Rosenberg, The University of Western Australia, Mark evaluations for an ensemble learning problem with up to 500 Reynolds, The University of Western Australia, Tim French, The variables. University of Western Australia, Lyndon While, The University of Western Australia Entropy-Based Evolutionary Diversity Optimisation for the In this paper we address the Euclidean Steiner tree problem in Traveling Salesperson Problem the plane in the presence of soft and solid polygonal obstacles. Adel Nikfarjam, The University of Adelaide, Jakob Bossek, Uni- The Euclidean Steiner tree problem is a well-known NP-hard versity of Münster, Aneta Neumann, The University of Ade- problem with different applications in network design. Given a laide, Frank Neumann, The University of Adelaide set of terminal nodes in the plane the aim is to find a shortest- Computing diverse sets of high-quality solutions has gained in- length interconnection of the terminals allowing further nodes, creasing attention among the evolutionary computation com- so-called Steiner points, to be added. In many real-life scenar- munity in recent years. It allows practitioners to choose from ios there are further constraints that need to be considered. a set of high-quality alternatives. In this paper, we employ a Regions in the plane that cannot be traversed or can only be population diversity measure, called the High-order entropy traversed at a higher cost can be approximated by polygonal measure, in an evolutionary algorithm to compute a diverse areas that either need to be avoided (solid obstacles) or come set of high-quality solutions for the Traveling Salesperson Prob- with a higher cost of traversing (soft obstacles). We propose a lem. In contrast to the previous studies, our approach allows genetic algorithm that uses problem-specific representation diversifying segments of tours containing several edges based and operators to solve this problem and show that the algo- on the entropy measure. We examine the resulting evolution- rithm can solve various test scenarios of different sizes. The ary diversity optimisation approach precisely in terms of the presented approach appears to outperform current heuristic final set of solutions and theoretical properties. Experimental approaches for the Steiner tree problem with soft obstacles and results show significant improvements compared to a recently was evaluated on larger test instances as well. proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments. Direct linkage discovery with empirical linkage learning Michal Przewozniczek, Wroclaw University of Science and Breeding Diverse Packings for the Knapsack Problem by Technology, Marcin Komarnicki, Wroclaw University of Science Means of Diversity-Tailored Evolutionary Algorithms and Technology, Bartosz Frej, Wroclaw University of Science Jakob Bossek, University of Münster, Aneta Neumann, The and Technology University of Adelaide, Frank Neumann, The University of Ade- Problem decomposition is an important part of many state- laide of-the-art Evolutionary Algorithms (EAs). The quality of the In practise, it is often desirable to provide the decision-maker decomposition may be decisive for the EA effectiveness and with a rich set of diverse solutions of decent quality instead efficiency. Therefore, in this paper, we focus on the recent of just a single solution. In this paper we study evolutionary proposition of Linkage Learning based on Local Optimization diversity optimization for the knapsack problem (KP). Our goal (3LO). 3LO is an empirical linkage learning (ELL) technique and is to evolve a population of solutions that all have a profit of is proven never to report the false linkage. False linkage is one at least (1 ε) OPT , where OPT is the value of an optimal of the possible linkage defects and occurs when linkage marks − · 122 Abstracts

two independent genes as a dependent. Although thanks to the fully applied numerous times for creating Boolean functions problem decomposition quality, the use of 3LO may lead to ex- with good cryptographic properties. Still, the applicability of cellent results, its main disadvantage is its high computational such approaches was always limited as the cryptographic com- cost. This disadvantage makes 3LO not applicable to state-of- munity knows how to construct suitable Boolean functions the-art EAs that originally employed Statistical-based Linkage with deterministic algebraic constructions. Thus, evolutionary Learning (SLL) and frequently update the linkage information. results so far helped to increase the confidence that evolution- Therefore, we propose the Direct Linkage Empirical Discovery ary techniques have a role in cryptography, but at the same technique (DLED) that preserves 3LO advantages, reduces its time, the results themselves were seldom used. This paper costs, and we prove that it is precise in recognizing the direct considers a novel problem using evolutionary algorithms to linkage. The concept of direct linkage, which we identify in this improve Boolean functions obtained through algebraic con- paper, is related to the quality of the decomposition of over- structions. To this end, we consider a recent generalization of lapping problems. The results show that incorporating DLED Hidden Weight Boolean Function construction, and we show in three significantly different state-of-the-art EAs may lead to that evolutionary algorithms can significantly improve the cryp- promising results. tographic properties of the functions. Our results show that the genetic algorithm performs by far the best of all the con- Evolutionary Algorithms-assisted Construction of sidered algorithms and improves the nonlinearity property in Cryptographic Boolean Functions all Boolean function sizes. As there are no known algebraic Claude Carlet, University of Bergen, Domagoj Jakobovic, Uni- techniques to reach the same goal, we consider this applica- versity of Zagreb, Stjepan Picek, Delft University of Technology tion a step forward in accepting evolutionary algorithms as a powerful tool in the cryptography domain. In the last few decades, evolutionary algorithms were success-

General Evolutionary Computation and Hybrids

GECH 1 Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax Monday, July 12, 10:20-11:40 Room GECH Maxim Buzdalov, ITMO University, Carola Doerr, Sorbonne Université Parallel Differential Evolution Applied to Interleaving Generation with Precedence Evaluation of Tentative Most evolutionary algorithms have parameters, which allow a Solutions great flexibility in controlling their behavior and adapting them to new problems. To achieve the best performance, it is often Hayato Noguchi, Ritsumeikan University, Tomohiro Harada, needed to control some of the parameters during optimiza- Tokyo Metropolitan University, Ruck Thawonmas, Rit- tion, which gave rise to various parameter control methods. In sumeikan University recent works, however, similar advantages have been shown, This paper proposes a method to improve the CPU utilization and even proven, for sampling parameter values from certain, of parallel differential evolution (PDE) by incorporating the in- often heavy-tailed, fixed distributions. This produced a family terleaving generation mechanism. Previous research proposed of algorithms currently known as "fast evolution strategies" the interleaving generation evolutionary algorithm (IGEA) and and "fast genetic algorithms". However, only little is known its improved variants (iIGEA). IGEA reduces the computation so far about the influence of these distributions on the perfor- time by generating new offspring, which parents have been mance of evolutionary algorithms, and about the relationships determined even when all individuals have not evaluated. How- between (dynamic) parameter control and (static) parameter ever, the previous research only used a simple EA method, sampling. We contribute to the body of knowledge by present- which is not suitable for practical use. For this issue, this paper ing an algorithm that computes the optimal static distributions, explores the applicability of IGEA and iIGEA to practical EA which describe the mutation operator used in the well-known methods. In particular, we choose differential evolution (DE), simple (1 λ) evolutionary algorithm on a classic benchmark + which is widely used in real-world applications, and propose problem OneMax. We show that, for large enough population IGDE and its improved variant, iIGDE. We conduct experiments sizes, such optimal distributions may be surprisingly compli- to investigate the effectiveness of IGDE with several features of cated and counter-intuitive. We investigate certain properties the evaluation time on a simulated parallel computing environ- of these distributions, and also evaluate the performance re- ment. The experimental results reveal that the IGDE variants grets of the (1 λ) evolutionary algorithm using standard mu- + have higher CPU utilization than a simple PDE and reduce the tation operators. computation time required for optimization. Besides, iIGDE outperforms the original IGDE for all features of the evaluation time. Personalizing Performance Regression Models to Black-Box Abstracts 123

Optimization Problems A hybrid CP/MOLS Approach for Multi-Objective Tome Eftimov, Jožef Stefan Institute, Anja Jankovic, Sorbonne Imbalanced Classification Université, Gorjan Popovski, Jožef Stefan Institute, Carola Do- Nicolas Szczepanski, CRIStAL UMR 9189 - CNRS; University of err, Sorbonne Université, Peter Korošec, Jožef Stefan Institute Lille, Central Lille, Gilles Audemard, CRIL UMR 8188 - CNRS, Accurately predicting the performance of different optimiza- University of Lens, Laetitia Jourdan, CRIStAL UMR 9189 - tion algorithms for previously unseen problem instances is CNRS; University of Lille, Central Lille, Christophe Lecoutre, crucial for high-performing algorithm selection and configu- CRIL UMR 8188 - CNRS, University of Lens, Lucien Mousin, ration techniques. In the context of numerical optimization, CRIStAL UMR 9189 - CNRS, Lille Catholic University, Nadara- supervised regression approaches built on top of exploratory jen Veerapen, CRIStAL UMR 9189 - CNRS; University of Lille, landscape analysis are becoming very popular. From the point Central Lille of view of Machine Learning (ML), however, the approaches In solving partial classification problem, recent studies about are often rather naive, using default regression or classification multi-objective local search (MOLS) has led to new algorithms techniques without proper investigation of the suitability of offering high performance, particularly when the data are im- the ML tools. With this work, we bring to the attention of our balanced. However, improving MOLS algorithms is still a diffi- community the possibility to personalize regression models cult and tedious task. The concept of these works is therefore to to specific types of optimization problems. Instead of aiming use another solving method for the rule mining problem and ex- for a single model that works well across a whole set of possi- ploiting it within the multi-objective local search of the MOCA-I bly diverse problems, our personalized regression approach algorithm. For this purpose, we present in this paper a new acknowledges that different models may suite different types modeling thanks to the off-the-shelf constraint programming of problems. Going one step further, we also investigate the (CP) techniques. The orthogonality in the solving between CP impact of selecting not a single regression model per problem, and MOLS is justified by their strong differences. Indeed, the but personalized ensembles. We test our approach on predict- first one is a complete method based on the constraint filter- ing the performance of numerical optimization heuristics on ing whereas the second one is an incomplete method using a the BBOB benchmark collection. pareto dominance-based approach (DMLS). To counteract per- formance problems, we have designed a hybrid CP and MOLS algorithm able to solve the multi-objective imbalanced classi- Adaptive Scenario Subset Selection for Minimax Black-Box fication rule mining problem. The intuition is to apply CP on Continuous Optimization a smaller sub-problem to avoid taking up too much memory Atsuhiro Miyagi, Taisei Corporation, University of Tsukuba, space, and then to exploit some resulting solutions from CP in Kazuto Fukuchi, University of Tsukuba, RIKEN AIP, Jun the MOLS algorithm. Experimental results on real imbalanced Sakuma, University of Tsukuba, RIKEN AIP, Youhei Akimoto, datasets show that our hybrid approach is statistically more University of Tsukuba, RIKEN AIP efficient. We handle min–max black-box optimization problems in which the scenario variable to be maximized is discrete and the de- Coevolutionary Modeling of Cyber Attack Patterns and sign variable to be minimized is a continuous vector. To reduce Mitigations Using Public Datasets the number of objective function calls, which are assumed to Michal Shlapentokh-Rothman, MIT, Jonathan Kelly, MIT, Avi- be computationally expensive, we propose an approach that tal Baral, MIT, ALFA Group, Erik Hemberg, MIT CSAIL, ALFA samples a subset of scenarios at each iteration to approximate Group, Una-May O’Reilly, MIT the worst-case objective function and apply the covariance ma- trix adaptation evolution strategy to the approximated worst- The evolution of advanced persistent threats (APTs) spurs us case objective function. In addition, we develop an adapta- to explore computational models of coevolutionary dynamics tion mechanism for the probability of sampling each scenario. arising from efforts to secure cyber systems from them. In a Moreover, we introduce the notion of support scenarios to first for evolutionary algorithms, we incorporate known threats characterize min–max optimization problems with discrete and vulnerabilities into a stylized "competition� that pits scenario variables and design test problems with various char- cyber attack patterns against mitigations. Variations of attack acteristics of support scenarios. Empirical evaluations reveal patterns that are drawn from the public CAPEC catalog offer- that the proposed approach learns to sample the set of support ing Common Attack Pattern Enumeration and Classifications. scenarios, being more efficient than sampling all the scenarios, Mitigations take two forms: soft- ware updates or monitor- especially when the scenarios outnumber the support scenar- ing, and the software that is mitigated is identified by drawing ios. from the public CVE dictionary of Common Vulnerabilities and Exposures. In another first, we quantify the outcome of a competition by incorporating the public Common Vulnerabil- GECH 2 ity Scoring System - CVSS. We align three abstract models of population-level dynamics where APTs interact with defenses Monday, July 12, 14:20-15:40 Room GECH with three competitive, coevolutionary algorithm variants that 124 Abstracts

use the competition. A comparative study shows that the way predictions. The surrogate’s involvement is determined by up- a defensive population preferentially acts, e.g. shifting to miti- dating a replacement probability based on the accuracy from gating recent attack patterns, results in different evolutionary past iterations. A study of four well-known population-based outcomes, expressed as different dominant attack patterns and optimization algorithms with and without the proposed proba- mitigations. bilistic surrogate assistance indicates its usefulness in achiev- ing a better convergence. The proposed framework enables Directing Evolution: The Automated Design of Evolutionary the incorporation of surrogates into an existing optimization Pathways Using Directed Graphs algorithm and, thus, paves the way for new surrogate-assisted algorithms dealing with challenges in less frequently addressed Braden Tisdale, Auburn University, Deacon Seals, Auburn Uni- computationally expensive functions, such as different variable versity, Aaron Pope, Los Alamos National Laboratory, Daniel types, large dimensional problems, multiple objectives, and Tauritz, Auburn University constraints. As computing power grows, the automated specialization and design of evolutionary algorithms (EAs) to tune their perfor- mance to individual problem classes becomes more attrac- EML 1 + GECH 3 + NE 2 - BP tive. To this end, a significant amount of research has been Tuesday, July 13, 12:20-13:40 Rooms GECH, EML, NE conducted in recent decades, utilizing a wide range of tech- niques. However, few techniques have been devised which Expressivity of Parameterized and Data-driven automate the design of the overall structure of an EA. Most Representations in Quality Diversity Search EA implementations rely solely on the traditional evolutionary Alexander Hagg, Bonn-Rhein-Sieg University of Applied Sci- cycle of parent selection, reproduction, and survival selection, ences, Leiden Institute of Advanced Computer Science, Sebas- and those with unique structures typically replace this with tian Berns, Queen Mary University of London, Alexander Aster- another static, hand-made cycle. Existing techniques for modi- oth, Bonn-Rhein-Sieg University of Applied Sciences, Simon fying the evolutionary structure use representations which are Colton, Queen Mary University of London, Monash University, either loosely structured and highly stochastic, or which are Thomas Bäck, Leiden Institute of Advanced Computer Science constrained and unable to easily evolve complicated pathways. We consider multi-solution optimization and generative mod- The ability to easily evolve complex evolutionary pathways els for the generation of diverse artifacts and the discovery would greatly expand the heuristic space which can be explored of novel solutions. In cases where the domain�s factors of during specialization, potentially allowing for the representa- variation are unknown or too complex to encode manually, tion of EAs which outperform the traditional cycle. This work generative models can provide a learned latent space to ap- proposes a methodology for the automated design of the evo- proximate these factors. When used as a search space, however, lutionary process by introducing a novel directed-graph-based the range and diversity of possible outputs are limited to the representation, created to be mutable and flexible, permitting expressivity and generative capabilities of the learned model. a black-box designer to produce reusable, high-performance We compare the output diversity of a quality diversity evolu- EAs. Experiments show that our methodology can produce tionary search performed in two different search spaces: 1) a high-performance EAs demonstrating intelligible strategies. predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an PSAF: A Probabilistic Surrogate-Assisted Framework for explicit parametric encoding creates more diverse artifact sets Single-Objective Optimization than searching the latent space. A learned model is better at Julian Blank, Michigan State University, Kalyanmoy Deb, Michi- interpolating between known data points than at extrapolating gan State University or expanding towards unseen examples. We recommend using In the last two decades, significant effort has been made to a generative model’s latent space primarily to measure simi- solve computationally expensive optimization problems using larity between artifacts rather than for search and generation. surrogate models. Regardless of whether surrogates are the Whenever a parametric encoding is obtainable, it should be primary drivers of an algorithm or improve the convergence of preferred over a learned representation as it produces a higher an existing method, most proposed concepts are rather specific diversity of solutions. and not very generalizable. Some important considerations are selecting a baseline optimization algorithm, a suitable sur- GECH 4 + IMPACT rogate methodology, and the surrogate’s involvement in the overall algorithm design. This paper proposes a probabilistic Wednesday, July 14, 12:20-13:40 Room GECH surrogate-assisted framework (PSAF), demonstrating its appli- cability to a broad category of single-objective optimization The Impact of Hyper-Parameter Tuning for methods. The framework injects knowledge from a surrogate Landscape-Aware Performance Regression and Algorithm into an existing algorithm through a tournament-based proce- Selection dure and continuing the optimization run on the surrogate’s Anja Jankovic, Sorbonne Université, Tome Eftimov, Jožef Stefan Abstracts 125

Institue, Gorjan Popovski, Jožef Stefan Institue, Carola Doerr, University of Science and Technology of China, Qiaozhi Zhang, CNRS, Sorbonne Université Southern University of Science and Technology, Huanhuan Automated algorithm selection and configuration methods that Chen, University of Science and Technology of China, Xin Yao, build on exploratory landscape analysis (ELA) are becoming Southern University of Science and Technology very popular in Evolutionary Computation. However, despite Many real-world applications have the time-linkage property, a significantly growing number of applications, the underly- and the only theoretical analysis is recently given by Zheng, ing machine learning models are often chosen in an ad-hoc et al. (TEVC 2021) on their proposed time-linkage OneMax manner. We show in this work that three classical regression problem, OneMax(0,1n ). However, only two elitist algorithms methods are able to achieve meaningful results for ELA-based (1+1)EA and (µ 1)EA are analyzed, and it is unknown whether + algorithm selection. For those three models – random forests, the non-elitism mechanism could help to escape the local op- decision trees, and bagging decision trees – the quality of the tima existed in OneMax(0,1n ). In general, there are few theo- regression models is highly impacted by the chosen hyper- retical results on the benefits of the non-elitism in evolution- parameters. This has significant effects also on the quality of ary algorithms. In this work, we analyze on the influence of the algorithm selectors that are built on top of these regres- the non-elitism via comparing the performance of the elitist sions. By comparing a total number of 30 different models, (1 λ)EA and its non-elitist counterpart (1,λ)EA. We prove that + each coupled with 2 complementary regression strategies, we with a high probability (1 λ)EA will get stuck in the local op- + derive guidelines for the tuning of the regression models and tima and cannot find the global optimum, but with probabil- provide general recommendations for a more systematic use ity 1, (1,λ)EA can reach the global optimum and its expected 3 c of classical machine learning models in landscape-aware al- runtime is O(n + logn) with λ c log e n for the constant = e 1 gorithm selection. We point out that a choice of the machine c 1. Noting that a smaller offspring size− is helpful for escap- ≥ learning model merits to be carefully undertaken and further ing from the local optima, we further resort to the compact investigated. genetic algorithm where only two individuals are sampled to update the probabilistic model, and prove its expected runtime When Non-Elitism Meets Time-Linkage Problems of O(n3 logn). Our computational experiments also verify the Weijie Zheng, Southern University of Science and Technology, efficiency of the two non-elitist algorithms.

Genetic Programming

GP 1 - BP PSB2: The Second Program Synthesis Benchmark Suite Monday, July 12, 14:20-15:40 Room GP Thomas Helmuth, Hamilton College, Peter Kelly, Hamilton College Towards Effective GP Multi-Class Classification Based on For the past six years, researchers in genetic programming and Dynamic Targets other program synthesis disciplines have used the General Pro- Stefano Ruberto, Joint Research Centre, Valerio Terragni, The gram Synthesis Benchmark Suite to benchmark many aspects University of Auckland, Jason Moore, University of Pennsylva- of automatic program synthesis systems. These problems have nia been used to make notable progress toward the goal of general In the multi-class classification problem GP plays an important program synthesis: automatically creating the types of soft- role when combined with other non-GP classifiers. However, ware that human programmers code. Many of the systems when GP performs the actual classification (without relying on that have attempted the problems in the original benchmark other classifiers) its classification accuracy is low. This is espe- suite have used it to demonstrate performance improvements cially true when the number of classes is high. In this paper, granted through new techniques. Over time, the suite has grad- we present DTC, a GP classifier that leverages the effectiveness ually become outdated, hindering the accurate measurement of the dynamic target approach to evolve a set of discriminant of further improvements. The field needs a new set of more functions (one for each class). Notably, DTC is the first GP difficult benchmark problems to move beyond what was previ- classifier that defines the fitness of individuals by using the ously possible. In this paper, we describe the 25 new general synergistic combination of linear scaling and the hinge-loss program synthesis benchmark problems that make up PSB2, function (commonly used by SVM). Differently, most previous a new benchmark suite. These problems are curated from a GP classifiers use the number of correct classifications to drive variety of sources, including programming katas and college the evolution. We compare DTC with eight state-of-art multi- courses. We selected these problems to be more difficult than class classification techniques (e.g., RF,RS, MLP,and SVM) on those in the original suite, and give results using PushGP show- eight popular datasets. The results show that DTC achieves ing this increase in difficulty. These new problems give plenty competitive classification accuracy even with 15 classes, with- of room for improvement, pointing the way for the next six or out relying on other classifiers. more years of general program synthesis research. 126 Abstracts

A Generalizability Measure for Program Synthesis with observe that the Moore neighborhood works better, although Genetic Programming the Von Neumann neighborhood allows for a larger training set. Dominik Sobania, University of Mainz, Franz Rothlauf, Univer- sity of Mainz Evolvability and Complexity Properties of the Digital The generalizability of programs synthesized by genetic pro- Circuit Genotype-Phenotype Map gramming (GP) to unseen test cases is one of the main chal- Alden Wright, University of Montana, Missoula, MT, USA, lenges of GP-based program synthesis. Recent work showed Cheyenne Laue, University of Montana, Missoula, MT that increasing the amount of training data improves the gen- eralizability of the programs synthesized by GP.However, gen- Recent research on genotype-phenotype (G-P) maps in natural erating training data is usually an expensive task as the output evolution has contributed significantly to our understanding value for every training case must be calculated manually by of neutrality, redundancy, robustness, and evolvability. Here the user. Therefore, this work suggests an approximation of the we investigate the properties of the digital logic gate G-P map expected generalization ability of solution candidates found and show that this map shares many of the common properties by GP.To obtain candidate solutions that all solve the training of natural G-P maps, with the exception of a positive relation- cases, but are structurally different, a GP run is not stopped ship between evolvability and robustness. Our results show after the first solution is found that solves all training instances that in some cases robustness and evolvability may be nega- but search continues for more generations. For all found can- tively related as a result of the method used to approximate didate solutions (solving all training cases), we calculate the evolvability. We give two definitions of circuit complexity and behavioral vector for a set of randomly generated additional empirically show that these two definitions are closely related. inputs. The proportion of the number of different found can- This study leads to a better understanding of the relationships didate solutions generating the same behavioral vector with between redundancy, robustness, and evolvability in genotype- highest frequency compared to all other found candidate solu- phenotype maps. We further investigate these results in the tions with different behavior can serve as an approximation for context of complexity and show the relationships between phe- the generalizability of the found solutions. The paper presents notypic complexity and phenotypic redundancy, robustness experimental results for a number of standard program synthe- and evolvability. sis problems confirming the high prediction accuracy. Zoetrope Genetic Programming for regression Aurelie Boisbunon, MyDataModels, Carlo Fanara, MyDataMod- GP 2 els, Ingrid Grenet, MyDataModels, Jonathan Daeden, MyData- Tuesday, July 13, 14:20-15:40 Room GP Models, Alexis Vighi, MyDataModels, Marc Schoenauer, Inria The Zoetrope Genetic Programming (ZGP) algorithm is based CoInGP: Convolutional Inpainting with Genetic on an original representation for mathematical expressions, Programming targetting evolutionary symbolic regression. The zoetropic rep- Domagoj Jakobovic, University of Zagreb, Luca Manzoni, Uni- resentation uses repeated fusion operations between partial versity of Trieste, Luca Mariot, Delft University of Technology, expressions, starting from the terminal set. Repeated fusions Stjepan Picek, Delft University of Technology, Mauro Castelli, within an individual gradually generate more complex expres- Universidade Nova de Lisboa sions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. We investigate the use of Genetic Programming (GP) as a con- ZGP individuals then undergo specific crossover and muta- volutional predictor for missing pixels in images. The training tion operators, and selection takes place between parents and phase is performed by sweeping a sliding window over an im- offspring. ZGP is validated using a large number of public do- age, where the pixels on the border represent the inputs of a main regression datasets, and compared to other symbolic re- GP tree. The output of the tree is taken as the predicted value gression algorithms, as well as to traditional machine learning for the central pixel. We consider two topologies for the sliding algorithms. ZGP reaches state-of-the-art performances with window, namely the Moore and the Von Neumann neighbor- respect to both types of algorithms, and demonstrates a low hood. The best GP tree scoring the lowest prediction error over computational time compared to other symbolic regression the training set is then used to predict the pixels in the test set. approaches. We experimentally assess our approach through two experi- ments. In the first one, we train a GP tree over a subset of 1000 Measuring Feature Importance of Symbolic Regression complete images from the MNIST dataset. The results show Models Using Partial Effects that GP can learn the distribution of the pixels with respect to a simple baseline predictor, with no significant differences Guilherme Aldeia, UFABC, Fabricio de Franca, UFABC observed between the two neighborhoods. In the second exper- In explainable AI, one aspect of a prediction’s explanation is to iment, we train a GP convolutional predictor on two degraded measure each predictor’s importance to the decision process. images, removing around 20% of their pixels. In this case, we The importance can measure how much variation a predictor Abstracts 127

promotes locally or how much the predictor contributes to the Genetic Programming (GP) is known to suffer from the burden deviation from a reference point (Shapley value). If we have the of being computationally expensive by design. While, over the ground truth analytical model, we can calculate the former us- years, many techniques have been developed to mitigate this ing the Partial Effect, calculated as the predictor’s partial deriva- issue, data vectorization, in particular, is arguably still the most tive. Also, we can estimate the latter by calculating the average attractive strategy due to the parallel nature of GP.In this work, partial effect multiplied by the difference between the predic- we employ a series of benchmarks meant to compare both tor and the reference value. Symbolic Regression is a gray-box the performance and evolution capabilities of different vector- model for regression problems that returns an analytical model ized and iterative implementation approaches across several approximating the input data. Although it is often associated existing frameworks. Namely, TensorGP,a novel open-source with interpretability, few works explore this property. This pa- engine written in Python, is shown to greatly benefit from the per will investigate the use of Partial Effect with the analytical TensorFlow library to accelerate the domain evaluation phase models generated by the Interaction-Transformation Evolu- in GP.The presented performance benchmarks demonstrate tionary Algorithm symbolic regressor (ITEA). We show that the that the TensorGP engine manages to pull ahead, with relative regression models returned by ITEA coupled with Partial Ef- speedups above two orders of magnitude for problems with a fect provide the closest explanations to the ground-truth and higher number of fitness cases. Additionally, as a consequence a close approximation to Shapley values. These results open of being able to compute larger domains, we argue that Ten- up new opportunities to explain symbolic regression models sorGP performance gains aid the discovery of more accurate compared to the approximations provided by model-agnostic candidate solutions. approaches. Cooperative Coevolutionary Multiobjective Genetic Programming for Microarray Data Classification GP 3 Yang Qing, Shenzhen University, Chi Ma, Shenzhen Univer- Wednesday, July 14, 10:20-11:40 Room GP sity, Yu Zhou, Shenzhen University, Xiao Zhang, College of Computer Science, South-Central University for Nationalities, Genetic Programming is Naturally Suited to Evolve Bagging Wuhan 430074, China, Haowen Xia, Shenzhen University Ensembles DNA microarray data contain valuable biological information, Marco Virgolin, Chalmers University of Technology which makes it of great importance in disease analysis and can- Learning ensembles by bagging can substantially improve the cer diagnosis. However, the classification of microarray data generalization performance of low-bias, high-variance estima- is still a challenging task because of the high dimension and tors, including those evolved by Genetic Programming (GP). To small sample size, especially for multiclass data accompanied be efficient, modern GP algorithms for evolving (bagging) en- by class imbalance. In this paper, we propose a cooperative co- sembles typically rely on several (often inter-connected) mech- evolutionary multiobjective genetic programming (CC-MOGP) anisms and respective hyper-parameters, ultimately compro- for microarray data classification. It converts a multiclass prob- mising ease of use. In this paper, we provide experimental lem into a set of tractable binary problems and coevolves the evidence that such complexity might not be warranted. We corresponding population. And a cooperative coevolutionary show that minor changes to fitness evaluation and selection Pareto archived evolution strategy (CCPAES) is employed to are sufficient to make a simple and otherwise-traditional GP approximate the Pareto front. During this procedure, we pro- algorithm evolve ensembles efficiently. The key to our pro- pose a synergy test method to assist in guiding the coevolution posal is to exploit the way bagging works to compute, for each between populations. Experimental results on 8 multiclass individual in the population, multiple fitness values (instead microarray data show that CC-MOGP can obtain competitive of one) at a cost that is only marginally higher than the one prediction accuracy compared with several state-of-art evolu- of a normal fitness evaluation. Experimental comparisons on tionary computation and traditional methods. classification and regression tasks taken and reproduced from prior studies show that our algorithm fares very well against A Novel Multi-Task Genetic Programming Approach to state-of-the-art ensemble and non-ensemble GP algorithms. Uncertain Capacitated Arc Routing Problem We further provide insights into the proposed approach by (i) Mazhar Ansari Ardeh, Victori University of Wellington, Yi Mei, scaling the ensemble size, (ii) ablating the changes to selection, Victori University of Wellington, Mengjie Zhang, Victori Uni- (iii) observing the evolvability induced by traditional subtree versity of Wellington variation. Code: https://github.com/marcovirgolin/2SEGP. Uncertain Capacitated Arc Routing Problem (UCARP) is an NP-hard optimisation problem with many applications in lo- Speed Benchmarking of Genetic Programming Frameworks gistics domains. Genetic Programming (GP) is capable of evolv- Francisco Baeta, University of Coimbra, João Correia, Univer- ing routing policies to handle the uncertain environment of sity of Coimbra, Tiago Martins, University of Coimbra, Pe- UCARP.There are many different but related UCARP domains nousal Machado, University of Coimbra in the real world to be solved (e.g. winter gritting and waste 128 Abstracts

collection for different cities). Instead of training a routing the uniqueness of transferable knowledge, as well as its quality, policy for each of them, we can use the multi-task learning into consideration. Additionally, the transferred knowledge is paradigm to improve the training effectiveness by sharing the utilised in a manner that improves diversity. We investigated common knowledge among the related UCARP domains. Pre- the performance of the proposed method with several experi- vious studies showed that GP population for solving UCARP mental studies and demonstrated that the designed knowledge loses diversity during its evolution, which decreases the effec- transfer mechanism can significantly improve the performance tiveness of knowledge sharing. To address this issue, in this of GP for solving UCARP. work we propose a novel multi-task GP approach that takes

Hot Off the Press

HOP 1 EA is able to reach the optimum. This paper for the Hot-off- the-Press track at GECCO 2021 summarizes the work “Analysis Monday, July 12, 10:20-11:40 Room HOP of Evolutionary Algorithms on Fitness Function with Time- On Sampling Error in Evolutionary Algorithms linkage Property” by W. Zheng, H. Chen, and X. Yao, which has been accepted for publication in the IEEE Transactions on Dirk Schweim, Johannes Gutenberg University, David Witten- Evolutionary Computation 2021. berg, Johannes Gutenberg University, Franz Rothlauf, Johannes Gutenberg University Realistic Constrained Multiobjective Optimization The initial population in evolutionary algorithms (EAs) should Benchmark Problems from Design form a representative sample of all possible solutions (the search space). While large populations accurately approximate Cyril Picard, École Polytechnique Fédérale de Lausanne, Jürg the distribution of possible solutions, small populations tend to Schiffmann, École Polytechnique Fédérale de Lausanne incorporate a sampling error. A low sampling error at initializa- Multiobjective optimization is applied in engineering to de- tion is necessary (but not sufficient) for a reliable search since sign new systems and to identify design tradeoffs. Yet, design a low sampling error reduces the overall random variations in problems often have objective functions and constraints that a random sample. For this reason, we have recently presented are expensive and highly nonlinear. Features lead to poor con- a model to determine a minimum initial population size so vergence and diversity loss with common algorithms. Current that the sampling error is lower than a threshold, given a con- constrained benchmark problems do not necessarily repre- fidence level. Our model allows practitioners of, for example, sent the challenges of engineering problems. In this work, a genetic programming (GP) and other EA variants to estimate a framework to design electro-mechanical actuators, called mul- reasonable initial population size. tiobjective design of actuators (MODAct), is presented and 20 constrained multiobjective optimization problems are derived Analysis of Evolutionary Algorithms on Fitness Function from the framework with a specific focus on constraints. The with Time-linkage Property (Hot-off-the-Press Track at full source code is made available to ease its use. A constraint GECCO 2021) landscape analysis approach is followed and extended with Weijie Zheng, Southern University of Science and Technology, three new metrics to characterize the search and objective University of Science and Technology of China, Huanhuan spaces. MODAct is compared to existing test suites to high- Chen, University of Science and Technology of China, Xin Yao, light the differences. In addition, a convergence analysis using Southern University of Science and Technology NSGA-II, NSGA-III, and C-TAEA on MODAct and existing test suites suggests that the design problems are indeed difficult In real-world applications, many optimization problems have due to the constraints. In particular, the number of simul- the time-linkage property, that is, the objective function value taneously violated constraints in newly generated solutions relies on the current solution as well as the historical solutions. seems key in understanding the convergence challenges. Thus, Although the rigorous theoretical analysis on evolutionary algo- MODAct offers an efficient framework to analyze and consider rithms has rapidly developed in the last two decades, it remains constraints in future optimization algorithm design. an open problem to theoretically understand the behaviors of evolutionary algorithms on time-linkage problems. This paper takes the first step towards the rigorous analyses of evolution- Achieving Weight Coverage for an Autonomous Driving ary algorithms for time-linkage functions. Based on the basic System with Search-based Test Generation (HOP Track at OneMax function, we propose a time-linkage function where GECCO 2021) the first bit value of the last time step is integrated but has a Thomas Laurent, Lero & University College Dublin, Paolo Ar- different preference from the current first bit. We prove that caini, National Institute of Informatics, Fuyuki Ishikawa, Na- with probability 1-o(1), randomized local search and (1+1) EA tional Institute of Informatics, Anthony Ventresque, Lero & cannot find the optimum, and with probability 1-o(1), (µ+1) University College Dublin Abstracts 129

Autonomous Driving Systems (ADSs) are complex critical sys- The paper provides significant evidence that the selective pres- tems that must be thoroughly tested. Still, assessing the sure of traditionally used steady state evolutionary algorithms strength of tests for ADSs is an open and complex problem. is much higher than desired for the effective optimisation of Weight Coverage is a test criterion targeting ADSs which are multimodal combinatorial optimisation problems. In partic- based on a weighted cost function. It measures how much ular, since steady-state evolutionary algorithms use selection each weight, related to different aspects of the ADS’s decision for replacement, it is not necessary to prefer better individu- process, is involved in the decisions taken in a test scenario. als during parent selection. This paper shows that a decrease Although weight coverage can measure the quality of a test of the parent selective pressure below uniform can be simply suite, it doesn’t provide clear guidance for test generation. This achieved by preferring worse individuals over better ones as work proposes weight coverage-driven search-based test gener- parents. Both theoretical and experimental evidence is pro- ation for ADSs. It describes and compares three designs of the vided that such inverse selection leads to faster optimisation search process: a single-objective search aiming at generating for several hard multimodal benchmark functions and clas- a test covering a single weight; a multi-objective search where sical combinatorial optimisation problems. The paper also each objective targets a different weight; and a single-objective reveals a particular power of the steady state model over the search where the fitness is an aggregate function represent- more traditional generational one, since achieving such low ing the coverage over multiple weights. Experiments using an selective pressure within generational evolutionary algorithms industrial ADS show the validity of the method and provide is prohibitive if not impossible. insights into the benefits of each search design. Original paper: T. Laurent, P.Arcaini, F.Ishikawa, A. Ventresque, ): Harmless Overfitting: Using Denoising Autoencoders in “Achieving Weight Coverage for an Autonomous Driving System Estimation of Distribution Algorithms with Search-based Test Generation”, 25th International Confer- Malte Probst, Honda Research Institute EU Carl-Legien-Str. 30, ence on Engineering of Complex Computer Systems (ICECCS 63073 Offe, Franz Rothlauf, Universität Mainz 2020). Estimation of Distribution Algorithms (EDAs) are metaheuris- tics where learning a model and sampling new solutions re- Interactive Parameter Tuning of Bi-objective Optimisation places the variation operators recombination and mutation Algorithms Using the Empirical Attainment Function used in standard Genetic Algorithms. The choice of these mod- els as well as the corresponding training processes are subject Manuel López-Ibáñez, Universidad de Málaga, Juan Diaz, Uni- to the bias/variance tradeoff, also known as under- and over- versidad San Francisco de Quito fitting: simple models cannot capture complex interactions We propose a visual approach of eliciting preferences from a De- between problem variables, whereas complex models are sus- cision Maker (DM) in the context of comparing the stochastic ceptible to modeling random noise. This paper suggests using outcomes of two alternative designs or parameter configura- Denoising Autoencoders (DAEs) as generative models within tions of an optimization algorithm for bi-objective problems. EDAs (DAE-EDA). The resulting DAE-EDA is able to model com- Our proposal is based on visualizing the differences between plex probability distributions. Furthermore, overfitting is less the empirical attainment functions (EAFs) of the two alterna- harmful, since DAEs overfit by learning the identity function. tive algorithmic configurations, and then ask the DM to choose This overfitting behavior introduces unbiased random noise their preferred side of the differences. Information about the into the samples, which is no major problem for the EDA but regions preferred by the DM is translated into a weighted hy- just leads to higher population diversity. As a result, DAE-EDA pervolume indicator that will assign higher quality values to runs for more generations before convergence and searches approximation fronts that result in EAF differences preferred promising parts of the solution space more thoroughly. We by the DM. This indicator may be used to guide an automatic study the performance of DAE-EDA on several combinatorial algorithm configuration method, such as irace, to search for single-objective optimization problems. In comparison to the parameter values that perform better in the objective space Bayesian Optimization Algorithm, DAE-EDA requires a similar regions preferred by the DM. Experiments on the well-known number of evaluations of the objective function but is much bi-objective quadratic assignment problem and a real-world faster and can be parallelized efficiently, making it the preferred production planning problem arising in the manufacturing choice especially for large and difficult optimization problems. industry show the benefits of the proposal.

HOP 2 On Steady-State Evolutionary Algorithms and Selective Monday, July 12, 14:20-15:40 Room HOP Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best The Influence of Uncertainties on Optimization of Dogan Corus, Kadir Has University, Pietro Oliveto, The Univer- Vaccinations on a Network of Animal Movements sity of Sheffield, Andrei Lissovoi, The University of Sheffield, Krzysztof Michalak, Wroclaw University of Economics, Mario Carsten Witt, Technical University of De Giacobini, University of Torino 130 Abstracts

This summary presents the results reported in the article root function into cube root, binary logarithm log2 and recipro- Krzysztof Michalak and Mario Giacobini, "The influence of cal square root. The GI cbrt is competitive in run-time perfor- uncertainties on optimization of vaccinations on a network of mance and our inverted square root x**-0.5 is far more accurate animal movements", which studies the influence of uncertain- than the approximation used in the Quake video game. We use ties on the effectiveness of vaccination schemes obtained by CMA-ES to adapt constants in a Newton-Raphson table, origi- using evolutionary algorithms and vaccination strategies. In nally from glibc’s sqrt, for other double precision mathematics this work the uncertainties are represented as unknown dis- functions. Such automatically customised math libraries might ease cases and changes introduced to the network of contacts be used for mobile or low resource, IoT, mote, smart dust, be- by a rewiring mechanism. The experiments show that evo- spoke cyber-physical systems. Evolutionary Computing (EC) lutionary algorithms outperform vaccination strategies when can be used to not only adapt source code but also data, such as provided information is accurate, that is, when most disease numerical constants, and could enable a new way to conduct cases are known and the intensity of rewiring is low. With software data maintenance. This is an exciting opportunity for higher level of uncertainty the strategies produce better results. the GECCO and optimisation communities. Results presented in the article motivate further work in sev- eral areas: modelling and prediction of changes in the contacts An Evolutionary Many-objective Approach to Multiview network, development of computational methods for estimat- Clustering Using Feature and Relational Data ing the number of initial disease cases, and hybridization of Adán José-García, Université de Lille, CINVESTAV-IPN, Julia evolutionary optimizers with vaccination strategies and other Handl, The University of Manchester, Wilfrido Gómez-Flores, knowledge-based approaches. Cinvestav-IPN, Mario Garza-Fabre, Cinvestav-IPN Many application domains involve the consideration of multi- Do Quality Indicators Prefer Particular Multi-Objective ple data sources. Typically, each of these data views provides a Search Algorithms in Search-Based Software Engineering? different perspective of a given set of entities. Inspired by early (Hot Off the Press track at GECCO 2021) work on multiview learning, multiview algorithms for data clus- Shaukat Ali, Simula Research Laboratory, Paolo Arcaini, Na- tering offer the opportunity to consider and integrate all this tional Institute of Informatics, Tao Yue, Nanjing University of information in an unsupervised setting. In practice, some com- Aeronautics and Astronautics, China and Simula Research Lab- plex real-world problems may give rise to a handful or more oratory, Norway data views, each with different reliability levels. However, ex- In Search-based Software Engineering (SBSE), researchers and isting algorithms are often limited to the consideration of two practitioners (SBSE users) using multi-objective search algo- views only, or they assume that all the views have the same level rithms (MOSAs) often select commonly used MOSAs to solve of importance. Here, we describe the design of an evolutionary their search problems. Such a selection is usually not justified, algorithm for the problem of multiview cluster analysis. The and the main selection criterion is the MOSA popularity. On the method is capable of considering views that are represented in other hand, SBSE users are usually aware of the desired quali- the form of distinct feature sets, or distinct dissimilarity matri- ties of solutions of their search problem, captured by Quality ces, or a combination of the two. Our experimental results on Indicators (QIs). Consequently, to guide SBSE users in selecting standard benchmark datasets confirm that adopting a many- MOSAs for their specific SBSE problems, we study preference objective evolutionary algorithm addresses the limitations of relationships between QIs and MOSAs with an empirical eval- previous work and can easily scale to settings with four or more uation. Given a QI or a quality aspect (e.g., convergence), we data views. The final highlight of our paper is an illustration of suggest a MOSA that is highly likely to produce solutions repre- the potential of the approach in an application to breast lesion senting the QI or the quality aspect. Based on our experiments’ classification. results, we provide insights and suggestions for SBSE users to choose a MOSA based on experimental evidence. This is an Greed is Good: Exploration and Exploitation Trade-offs in extended abstract of the paper: S. Ali, P. Arcaini, and T. Yue, Bayesian Optimisation "Do Quality Indicators Prefer Particular Multi-Objective Search George De Ath, University of Exeter, Richard Everson, Uni- Algorithms in Search-Based Software Engineering?”, 12th Inter- versity of Exeter, Alma Rahat, Swansea University, Jonathan national Symposium on Search-Based Software Engineering Fieldsend, University of Exeter (SSBSE 2020). The performance of acquisition functions for Bayesian optimi- sation to locate the global optimum of continuous functions is Genetic Improvement of Data for Maths Functions investigated in terms of the Pareto front between exploration William Langdon, University College London, Oliver Krauss, and exploitation. We show that Expected Improvement (EI) and University of Applied Sciences Upper Austria the Upper Confidence Bound (UCB) always select solutions to Genetic Improvement (GI) can be used to give better quality be expensively evaluated on the Pareto front, but Probabil- software and to create new functionality. We show that GI can ity of Improvement is not guaranteed to do so and Weighted evolve the PowerPC open source GNU C runtime library square Expected Improvement does so only for a restricted range of Abstracts 131

weights. We introduce two novel \epsilon-greedy acquisition the ORP may be used successfully in genetic algorithms. This functions. Extensive empirical evaluation of these together abstract for the Hot-off-the-Press track of GECCO 2021 sum- with random search, purely exploratory, and purely exploita- marizes work that has appeared in Eremeev A.V., Kovalenko tive search on 10 benchmark problems in 1 to 10 dimensions Yu.V. A memetic algorithm with optimal recombination for the shows that \epsilon-greedy algorithms are generally at least asymmetric traveling salesman problem. Memetic Computing as effective as conventional acquisition functions (e.g. EI and 12, 23-36 (2020). UCB), particularly with a limited budget. In higher dimen- sions \epsilon-greedy approaches are shown to have improved HOP 3 performance over conventional approaches. These results are borne out on a real world computational fluid dynamics opti- Tuesday, July 13, 12:20-13:40 Room HOP misation problem and a robotics active learning problem. Our Reducing Bias in Multi-Objective Optimization analysis and experiments suggest that the most effective strat- Benchmarking egy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution. Tome Eftimov, Jožef Stefan Institute, Peter Korošec, Jožef Stefan Institute Multi-Objective Parameter-less Population Pyramid in The performance assessment of multi-objective optimization Solving the Real-World and Theoretical Problems algorithms involves a user-preference-based selection of a sin- gle quality indicator used as a performance measure. A sin- Michal Przewozniczek, Wroclaw University of Science and gle quality indicator maps the approximation set (i.e., high- Technology, Piotr Dziurzanski, West Pomeranian University dimensional data) into a real value (i.e, one-dimensional data). of Technology, Shuai Zhao, The University of York, Leandro However, it is well known that the selection of the quality in- Indrusiak, The University of York dicator can have a huge impact on the benchmarking conclu- Many real-world problems are notoriously multi-objective and sions. This invites researchers to present only results for quality NP-hard. Hence, there is a constant striving for optimizers ca- indicators that are in favor of the desired algorithm, or per- pable of solving such problems effectively. In this paper, we ex- forming bias performance assessment. To go beyond this, we amine the Multi-Objective Parameter-less Population Pyramid proposed a novel ranking scheme that reduces the bias in the (MO-P3). MO-P3 is based on the Parameter-less Population user-preference selection by comparing the high-dimensional Pyramid (P3) that was dedicated to solving single-objective data of approximations sets and consequently provides more problems. P3 employs linkage learning to decompose the prob- robust statistical results. The selection of a quality indicator lem and uses this information during its run. P3 maintains is only required in cases when high-dimensional distributions many different linkage information sets, which is the key to ef- of the approximation sets differ. By performing such analy- fectively solve the problems of the overlapping nature, i.e., the ses, experimental results show that the cases affected by the problems whose variables form a large and complicated net- user-preference selection are reduced. work of dependencies rather than additively separable blocks. MO-P3 inherits the features of its predecessor and employs Theoretical Analyses of Multi-Objective Evolutionary both linkage learning and linkage diversity maintenance to ef- Algorithms on Multi-Modal Objectives (Hot-off-the-Press fectively solve hard multi-objective problems, which includes Track at GECCO 2021) both: well-known test problems and NP-hard real-world prob- Benjamin Doerr, École Polytechnique, CNRS, Weijie Zheng, lems. Southern University of Science and Technology, University of Science and Technology of China Optimal Recombination and Adaptive Restarts Improve GA Previous theory work on multi-objective evolutionary algo- Performance on the Asymmetric TSP rithms considers mostly easy problems that are composed of Anton Eremeev, Institute of Scientific Information for Social Sci- unimodal objectives. This paper takes a first step towards a ences RAS, Dostoevsky Omsk State University, Yulia Kovalenko, deeper understanding of how evolutionary algorithms solve Institute of Scientific Information for Social Sciences RAS, Dos- multi-modal multi-objective problems. We propose the One- toevsky Omsk State University JumpZeroJump problem, a bi-objective problem with single We propose a new genetic algorithm (GA) with optimal recom- objectives isomorphic to the classic jump function benchmark. bination and adaptive restarts for the asymmetric travelling We prove that the simple evolutionary multi-objective opti- salesman problem (ATSP). The optimal recombination prob- mizer (SEMO) cannot compute the full Pareto front. In con- n lem (ORP) is solved in a crossover operator based on a new ex- trast, for all problem sizes n and all jump sizes k [4.. 1], the ∈ 2 − act algorithm that solves the ATSP on cubic digraphs. We use an global SEMO (GSEMO) covers the Pareto front in Θ((n 2k)nk ) − adaptive restart rule based on the Schnabel census estimate. A iterations in expectation. To improve the performance, we com- computational experiment on the known benchmark instances bine the GSEMO with two approaches, a heavy-tailed mutation shows that the proposed algorithm yields results competitive to operator and a stagnation detection strategy, that showed ad- those of state-of-the-art algorithms for ATSP and confirms that vantages in single-objective multi-modal problems. Runtime 132 Abstracts

improvements of asymptotic order at least kΩ(k) are shown for (Hot-off-the-Press Track at GECCO 2021) both strategies. Our experiments verify the substantial runtime Hao Jiang, Anhui University, Yuhang Wang, Anhui University, gains already for moderate problem sizes. Overall, these results Ye Tian, Anhui University, Xingyi Zhang, Anhui University, Jian- show that the ideas recently developed for single-objective hua Xiao, Nankai University evolutionary algorithms can be effectively employed also in The algorithm recommendation for capacitated vehicle rout- multi-objective optimization. This Hot-off-the-Press paper ing problems (CVRPs) usually determines the relationship be- summarizes “Theoretical Analyses of Multi-Objective Evolu- tween the features of CVRP instances and the performance of tionary Algorithms on Multi-Modal Objectives” by B. Doerr and algorithms, by which the algorithm recommendation method W. Zheng, which has been accepted for publication in AAAI can select the best solving algorithm from the algorithm li- 2021. brary. Therefore, the features used for characterizing CVRPs play an important role in algorithm recommendation. To this Genetic Improvement of Routing in Delay Tolerant end, three groups of penetrating features are proposed to cap- Networks ture the characteristics of CVRPs. The first group consists of Michela Lorandi, University of Trento, Leonardo Custode, Uni- some basic features of CVRPs. The second group is composed versity of Trento, Giovanni Iacca, University of Trento of the features extracted from some CVRP solutions gener- ated by local search. The third group is made up of image Routing plays a fundamental role in network applications, but features obtained by depicting CVRP instances through im- it is especially challenging in Delay Tolerant Networks (DTNs). ages, which is first introduced by us to enhance the general- These are a kind of mobile ad hoc networks made of e.g. (possi- ization of algorithm recommendation. Furthermore, based on bly, unmanned) vehicles and humans where, despite a lack of the three groups of features, an algorithm recommendation continuous connectivity, data must be transmitted while the method called ARM-I is built to recommend suitable algorithm network conditions change due to the nodes’ mobility. In these for CVRPs. contexts, routing is NP-hard and is usually solved by heuris- tic "store and forward" replication-based approaches, which however produce relatively low delivery probabilities. Here, Improving Assertion Oracles with Evolutionary we genetically improve two routing protocols widely adopted Computation in DTNs, namely Epidemic and PRoPHET, in the attempt to Valerio Terragni, University of Auckland, Gunel Jahangirova, optimize their delivery probability. First, we dissect them into Università della Svizzera italiana, Mauro Pezzè, Università della their fundamental components, i.e., functionalities such as Svizzera italiana & Schaffhausen Institute of Technology, Paolo checking if a node can transfer data, or sending messages to Tonella, Università della Svizzera italiana all connections. Then, we apply Genetic Improvement (GI) to Assertion oracles are executable boolean expressions placed manipulate these components as terminal nodes of evolving inside a software program that verify the correctness of test trees. We apply this methodology, in silico, to six test cases of executions. A perfect assertion oracle passes (returns true) for urban networks made of hundreds of nodes, and find that GI all correct executions and fails (returns false) for all incorrect produces consistent gains in delivery probability in four cases. executions. Because designing perfect assertion oracles is dif- ficult, assertions often fail to distinguish between correct and Runtime Analysis via Symmetry Arguments incorrect executions. In other words, they are prone to false (Hot-off-the-Press Track at GECCO 2021) positives and false negatives. GAssert is the first technique Benjamin Doerr, Ecole Polytechnique, Laboratoire to automatically improve assertion oracles by reducing false d’Informatique (LIX) positives and false negatives. Given an assertion oracle and a set of correct and incorrect program states, GAssert employs We use an elementary argument building on group actions to a novel co-evolutionary algorithm that explores the space of prove that the selection-free steady state genetic algorithm an- possible assertions to identify one with fewer false positives alyzed by Sutton and Witt (GECCO 2019) takes an expected and false negatives. Our evaluation on 34 Java methods shows number of Ω(2n/pn) iterations to find any particular target that GAssert effectively improves assertion oracles. search point. This bound is valid for all population sizes µ. Our result improves and extends the previous lower bound of Ω(exp(nδ/2)) valid for population sizes µ O(n1/2 δ), 0 δ On Bayesian Search for the Feasible Space Under = − < < 1/2. This paper for the Hot-off-the-Press track at GECCO 2021 Computationally Expensive Constraints summarizes the work Benjamin Doerr. Runtime Analysis of Alma Rahat, Swansea University, Michael Wood, ACT Acoustics Evolutionary Algorithms via Symmetry Arguments. Information Ltd Processing Letters, 166:106064. 2021. We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective Feature Construction for Meta-heuristic Algorithm design exploration. If determining feasibility required compu- Recommendation of Capacitated Vehicle Routing Problems tationally expensive simulations, the cost of exploration would Abstracts 133

be prohibitive. Bayesian search – a surrogate-assisted evolu- number of expensive evaluations, the models can accurately tionary approach – is data-efficient for such problems: starting predict the feasibility of any solution obviating the need for full from a small dataset, the central concept is to use Bayesian simulations. In this paper, we propose a novel acquisition func- surrogate models of expensive constraints with an acquisition tion that combines the probability that a solution is feasible or function to locate promising solutions that may improve pre- not (representing exploitation) and the entropy in predictions dictions of feasibility when the dataset is augmented. At the (representing exploration). Experiments confirmed the efficacy end of this sequential active learning approach with a limited of the proposed function.

Neuroevolution

NE 1 policies are located. This paper proposes a novel method for diversity-based policy search via Neuroevolution, that lever- Monday, July 12, 10:20-11:40 Room NE ages learned representations of the policy network parame- Using Novelty Search to Explicitly Create Diversity in ters, by performing policy search in this learned representation Ensembles of Classifiers space. Our method relies on the Quality-Diversity (QD) frame- work which provides a principled approach to policy search, Rui Cardoso, Imperial College London, Emma Hart, Edin- and maintains a collection of diverse policies, used as a dataset burgh Napier University, David Kurka, Imperial College Lon- for learning policy representations. Further, we use the Jaco- don, Jeremy Pitt, Imperial College London bian of the inverse-mapping function to guide the search in The diversity between individual learners in an ensemble is the representation space. This ensures that the generated sam- known to influence its performance. However, there is no stan- ples remain in the high-density regions, after mapping back to dard agreement on how diversity should be defined, and thus the original space. Finally, we evaluate our contributions on how to exploit it to construct a high-performing classifier. We four continuous-control tasks in simulated environments, and propose two new behavioural diversity metrics based on the di- compare to diversity-based baselines. vergence of errors between models. Following a neuroevolution approach, these metrics are then used to guide a novelty search Evolving and Merging Hebbian Learning Rules: Increasing algorithm to search a space of neural architectures and discover Generalization by Decreasing the Number of Rules behaviourally diverse classifiers, iteratively adding the models with high diversity score to an ensemble. The parameters of Joachim Pedersen, IT University of Copenhagen, Sebastian Risi, each ANN are tuned individually with a standard gradient de- IT University of Copenhagen scent procedure. We test our approach on three benchmark Generalization to out-of-distribution (OOD) circumstances af- datasets from Computer Vision — CIFAR-10, CIFAR-100, and ter training remains a challenge for artificial agents. To improve SVHN — and find that the ensembles generated significantly the robustness displayed by plastic Hebbian neural networks, outperform ensembles created without explicitly searching for we evolve a set of Hebbian learning rules, where multiple con- diversity and that the error diversity metrics we propose lead nections are assigned to a single rule. Inspired by the biological to better results than others in the literature. We conclude that phenomenon of the genomic bottleneck, we show that by allow- our empirical results signpost an improved approach to pro- ing multiple connections in the network to share the same local moting diversity in ensemble learning, identifying what sort learning rule, it is possible to drastically reduce the number of diversity is most relevant and proposing an algorithm that of trainable parameters, while obtaining a more robust agent. explicitly searches for it without selecting for accuracy. During evolution, by iteratively using simple K-Means clus- tering to combine rules, our Evolve & Merge approach is able Policy Manifold Search: Exploring the Manifold Hypothesis to reduce the number of trainable parameters from 61,440 to for Diversity-based Neuroevolution 1,920, while at the same time improving robustness, all without Nemanja Rakicevic, Imperial College London, Antoine Cully, increasing the number of generations used. While optimization Imperial College London, Petar Kormushev, Imperial College of the agents is done on a standard quadruped robot morphol- London ogy, we evaluate the agents’ performances on slight mor- phology modifications in a total of 30 unseen morphologies. Neuroevolution is an alternative to gradient-based optimisa- Our results add to the discussion on generalization, overfitting tion that has the potential to avoid local minima and allows and OOD adaptation. To create agents that can adapt to a wider parallelisation. The main limiting factor is that usually it does array of unexpected situations, Hebbian learning combined not scale well with parameter space dimensionality. Inspired with a regularising “genomic bottleneck” could be a promising by recent work examining neural network intrinsic dimension research direction. and loss landscapes, we hypothesise that there exists a low- dimensional manifold, embedded in the policy network param- eter space, around which a high-density of diverse and useful Genetic Crossover in the Evolution of Time-dependent 134 Abstracts

Neural Networks NE 3 Jason Orlosky, Osaka University, Augusta University, Tim Tuesday, July 13, 14:20-15:40 Room NE Grabowski, Independent Researcher Neural networks with temporal characteristics such as asyn- A Geometric Encoding for Neural Network Evolution chronous spiking have made much progress towards biolog- Paul Templier, ISAE-SUPAERO, University of Toulouse, Em- ically plausible artificial intelligence. However, genetic ap- manuel Rachelson, ISAE-SUPAERO, University of Toulouse, proaches for evolving network structures in this area are still Dennis Wilson, ISAE-SUPAERO, University of Toulouse relatively unexplored. In this paper, we examine a specific vari- A major limitation to the optimization of artificial neural net- ant of time-dependent spiking neural networks (NN) in which works (ANN) with evolutionary methods lies in the high dimen- the spatial and temporal relationships between neurons affect sionality of the search space, the number of weights growing output. First, we built and customized a standard NN imple- quadratically with the size of the network. This leads to expen- mentation to more closely model the timedelay characteristics sive training costs, especially in evolution strategies which rely of biological neurons. Next, we tested this with simulated tasks on matrices whose sizes grow with the number of genes. We such as food foraging and image recognition, demonstrating introduce a geometric encoding for neural network evolution success in multiple domains. We then developed a genetic (GENE) as a representation of ANN parameters in a smaller representation for the network that allows for both scalable space that scales linearly with the number of neurons, allowing network size and compatibility with genetic crossover oper- for efficient parameter search. Each neuron of the network ations. Finally, we analyzed the effects of genetic crossover is encoded as a point in a latent space and the weight of a algorithms compared to random mutations on the food forag- connection between two neurons is computed as the distance ing task. Results showed that crossover operations based on between them. The coordinates of all neurons are then opti- node usage converge on a local maximum more quickly than mized with evolution strategies in a reduced search space while random mutations, but suffer from genetic defects that reduce not limiting network fitness and possibly improving search. overall population performance.

Evolving Neural Architecture Using One Shot Model EML 1 + GECH 3 + NE 2 - BP Nilotpal Sinha, National Chiao Tung University, National Yang Tuesday, July 13, 12:20-13:40 Rooms GECH, EML, NE Ming Chiao Tung University, Kuan-Wen Chen, National Chiao Tung University, National Yang Ming Chiao Tung University Policy Gradient Assisted MAP-Elites Previous evolution based architecture search require high com- Olle Nilsson, Imperial College, Antoine Cully, Imperial College putational resources resulting in large search time. In this work, Quality-Diversity optimization algorithms such as MAP- we propose a novel way of applying a simple genetic algorithm Elites, aim to generate collections of both diverse and high- to the neural architecture search problem called EvNAS (Evolv- performing solutions to an optimization problem. MAP-Elites ing Neural Architecture using One Shot Model) which reduces has shown promising results in a variety of applications. In the search time significantly while still achieving better result particular in evolutionary robotics tasks targeting the genera- than previous evolution based methods. The architectures are tion of behavioral repertoires that highlight the versatility of represented by architecture parameter of one shot model which robots. However, for most robotics applications MAP-Elites results in the weight sharing among the given population of is limited to using simple open-loop or low-dimensional con- architectures and also weight inheritance from one generation trollers. Here we present Policy Gradient Assisted MAP-Elites to the next generation of architectures. We use the accuracy (PGA-MAP-Elites), a novel algorithm that enables MAP-Elites of partially trained architecture on validation data as a predic- to efficiently evolve large neural network controllers by intro- tion of its fitness to reduce the search time. We also propose ducing a gradient-based variation operator inspired by Deep a decoding technique for the architecture parameter which is Reinforcement Learning. This operator leverages gradient es- used to divert majority of the gradient information towards the timates obtained from a critic neural network to rapidly find given architecture and is also used for improving the fitness higher-performing solutions and is paired with a traditional prediction of the given architecture from the one shot model genetic variation to maintain a divergent search behavior. The during the search process. EvNAS searches for architecture on synergy of these operators makes PGA-MAP-Elites an efficient CIFAR-10 for 3.83 GPU day on a single GPU with top-1 test error yet powerful algorithm for finding diverse and high-performing 2.47%, which is then transferred to CIFAR-100 and ImageNet behaviors. We evaluate our method on four different tasks for achieving top-1 error 16.37% and top-5 error 7.4% respectively. building behavioral repertoires that use deep neural network controllers. The results show that PGA-MAP-Elites significantly Training Spiking Neural Networks with a Multi-Agent improves the quality of the generated repertoires compared to Evolutionary Robotics Framework existing methods. Souvik Das, Purdue University, Anirudh Shankar, Purdue Uni- versity, Vaneet Aggarwal, Purdue University Abstracts 135

We demonstrate the training of Spiking Neural Networks (SNN) Neural Architecture Search (NAS) is the name given to a set of in a novel multi-agent Evolutionary Robotics (ER) framework methods designed to automatically configure the layout of neu- inspired by competitive evolutionary environments in nature. ral networks. Their success on Convolutional Neural Networks The topology of a SNN along with morphological parameters of inspired its use on optimizing other types of neural network the bot it controls in the ER environment is together treated as a architectures, including Graph Neural Networks (GNNs). GNNs phenotype. Rules of the framework select certain bots and their have been extensively applied over several collections of real- SNNs for reproduction and others for elimination based on world data, achieving state-of-the-art results in tasks such as their efficacy in capturing food in a competitive environment. circuit design, molecular structure generation and anomaly While the bots and their SNNs are not explicitly trained to sur- detection. Many GNN models have been recently proposed, vive or reproduce using loss functions, these drives emerge and choosing the best model for each problem has become implicitly as they evolve to hunt food and survive. Their effi- a cumbersome and error-prone task. Aiming to alleviate this ciency in capturing food exhibits the evolutionary signature of problem, recent works have proposed strategies for applying punctuated equilibrium. We use this signature to compare the NAS to GNN models. However, different search methods con- performances of two evolutionary inheritance algorithms on verge relatively fast in the search for a good architecture, which the phenotypes, Mutation and Crossover with Mutation, using raises questions about the structure of the problem. In this ensembles of 100 experiments for each algorithm. We find that work we use Fitness Landscape Analysis (FLA) measures to Crossover with Mutation promotes 40% faster learning in the characterize the search space explored by NAS methods for SNN than mere Mutation with a statistically significant margin. GNNs. We sample almost 90k different architectures that cover most of the fitness range, and represent them using both a Fitness Landscape Analysis of Graph Neural Network one-hot encoding and an embedding representation. Results Architecture Search Spaces of the fitness distance correlation and dispersion metrics show Matheus Nunes, Universidade Federal de Minas Gerais, Paulo the fitness landscape is easy to be explored, and presents low Fraga, Universidade Federal de Minas Gerais, Gisele Pappa, neutrality. Universidade Federal de Minas Gerais

Real World Applications

RWA 1 - BP feature of a generated level computed through an AI agent’s play, while keeping the level stable and natural. Monday, July 12, 10:20-11:40 Room RWA

Level Generation for Angry Birds with Sequential VAE and Evolutionary Minimization of Traffic Congestion Latent Variable Evolution Maximilian Böther, Hasso Plattner Institute, University of Pots- Takumi Tanabe, University of Tsukuba, RIKEN AIP, Kazuto dam, Leon Schiller, Hasso Plattner Institute, University of Pots- Fukuchi, University of Tsukuba, RIKEN AIP, Jun Sakuma, Uni- dam, Philipp Fischbeck, Hasso Plattner Institute, University of versity of Tsukuba, RIKEN AIP, Youhei Akimoto, University of Potsdam, Louise Molitor, Hasso Plattner Institute, University of Tsukuba, RIKEN AIP Potsdam, Martin Krejca, Sorbonne University, Tobias Friedrich, Video game level generation based on machine learning (ML), Hasso Plattner Institute, University of Potsdam in particular, deep generative models, has attracted attention Traffic congestion is a major issue that can be solved by sug- as a technique to automate level generation. However, ap- gesting drivers alternative routes they are willing to take. This plications of existing ML-based level generations are mostly concept has been formalized as a strategic routing problem limited to tile-based level representation. When ML techniques in which a single alternative route is suggested to an existing are applied to game domains with non-tile-based level repre- one. We extend this formalization and introduce the Multiple sentation, such as Angry Birds, where objects in a level are Routes problem, which is given a start and destination and aims specified by real-valued parameters, ML often fails to generate at finding up to n different routes that the drivers strategically playable levels. In this study, we develop a deep-generative- disperse over, minimizing the overall travel time of the system. model-based level generation for the game domain of Angry Due to the NP-hard nature of the problem, we introduce the Birds. To overcome these drawbacks, we propose a sequential Multiple Routes evolutionary algorithm (MREA) as a heuristic encoding of a level and process it as text data, whereas existing solver. We study several mutation and crossover operators and approaches employ a tile-based encoding and process it as an evaluate them on real-world data of Berlin, Germany. We find image. Experiments show that the proposed level generator that a combination of all operators yields the best result, im- drastically improves the stability and diversity of generated proving the overall travel time by a factor between 1.8 and 3, in levels compared with existing approaches. We apply latent the median, compared to all drivers taking the fastest route. For variable evolution with the proposed generator to control the the base case n=2, we compare our MREA to the highly tailored 136 Abstracts

optimal solver by Bläsius et al. [ATMOS 2020] and show that, individual recorded frame. We distinguish and benchmark the in the median, our approach finds solutions of quality at least optimizers, in three different real-world scenarios, two of which 99.69% of an optimal solution while only requiring 40% of the are based on the geometric proximity of the template to the time. target in individual frames, while in the third one we fit the tem- plate sequentially to all target frames of the recorded sequence. RWA 2 Conclusions of this work can serve as a reference for future optimizer implementations and our findings can server as a Tuesday, July 13, 12:20-13:40 Room RWA baseline for future multi-objective optimization approaches. We make part of our benchmark and experiment setup A simple evolutionary algorithm guided by local mutations publicly available (https://github.com/VCL3D/nevergrad, for an efficient RNA design. https://github.com/VCL3D/PerformanceCapture/releases/). Cyrille Merleau Nono Saha, Max Planck Institute for Mathemat- icsin the Sciences, Matteo Smerlak, Max Planck Institute for Accelerated Evolutionary Induction of Heterogeneous Mathematicsin the Sciences Decision Trees for Gene Expression-Based Classification Inverse folding an RNA secondary structure of length L can Marcin Czajkowski, Bialystok University of Technology, Faculty be understood as searching in space S {A,C,G,U}L, one or = of Computer Science, Krzysztof Jurczuk, Bialystok University many RNA sequence(s) that fold(s) into that structure. Solving of Technology, Faculty of Computer Science, Marek Kretowski, this problem is a prerequisite for RNA design and is still a real Bialystok University of Technology, Faculty of Computer Sci- challenge in bioengineering. To improve the RNA inverse fold- ence ing, we investigate an evolutionary algorithm implementing local mutations taking into account the nucleotide and canoni- Decision trees (DTs) are popular techniques in the field of cal base pair distributions. We implement our algorithm as a eXplainable Artificial Intelligence. Despite their effective- simple open-source python program called “aRNAque”. The ness in solving various classification problems, they are not empirical results show that “aRNAque” can solve some target compatible with modern biological data generated with high- structures previously unsolvable by the existing evolutionary throughput technologies. This work aims to combine evo- algorithms benchmarked in this work. On the two benchmark lutionary induced DT with a recently developed concept de- datasets, 72% of the EteRNA100 dataset (respectively 92% using signed directly for gene expression data, called Relative eXpres- the Turner1999 energy parameter sets) and 87% of the non- sion Analysis (RXA). We propose a new solution, termed Evolu- EteRNA100 dataset and the designed sequences are of better tionary Heterogeneous Decision Tree (EvoHDTree), which uses quality. In comparison to existing tools, our EA implementation both classical univariate and bivariate tests that focus on the also shows a significant computational time improvement. relative ordering and weight relationships between the genes in the splitting nodes. The search for the decision tree structure, Zeroth-Order Optimizer Benchmarking for 3D node representation, and splits is performed globally by the Performance Capture evolutionary algorithm. To meet the huge computational de- mands, we enriched our solution with more than a dozen spe- Alexandros Doumanoglou, Centre For Research and Technol- cialized variants of recombination operators, GPU-computed ogy HELLAS, Petros Drakoulis, Centre For Research and Tech- local search components, OpenMP parallelization, and built-in nology HELLAS, Kyriaki Christaki, Centre For Research and gene ranking to improve evolutionary convergence. Experi- Technology HELLAS, Nikolaos Zioulis, Centre For Research ments performed on cancer-related gene expression-based and Technology HELLAS, Vladimiros Sterzentsenko, Centre For data show that the proposed solution finds accurate and much Research and Technology HELLAS, Antonis Karakottas, Cen- simpler interactions between genes. Importantly, the patterns tre For Research and Technology HELLAS, Dimitrios Zarpalas, discovered by EvoHDTree are easy to understand and to some Centre For Research and Technology HELLAS, Petros Daras, extent supported by biological evidence in the literature. Centre For Research and Technology HELLAS In the field of 3D Human Performance Capture, a high-quality Design of Specific Primer Sets for SARS-CoV-2 Variants 3D scan of the performer is rigged and skinned to an ani- Using Evolutionary Algorithms matable 3D template mesh that is subsequently fitted to the captured performance’s RGB-D data. Template fitting is ac- Alejandro Lopez Rincon, Utrecht University, Carmina Perez complished via solving for the template’s pose parameters that Romero, Universidad Central de Queretaro, Lucero Mendoza- better explain the performance data at each recorded frame. In Maldonado, Hospital Civil de Guadalajara ”Dr. Juan I. Men- this paper, we challenge open implementations of zeroth-order chaca”, Eric Claassen, Athena Institute, Vrije Universiteit, Jo- optimizers to solve the template fitting problem in a human han Garssen, Department of Immunology, Danone Nutricia performance capture dataset. The objective function that we research, Aletta Kraneveld, Utrecht University, Alberto Tonda, employ approximates, the otherwise costly to evaluate, 3D RMS INRAE hausdorff distance between the animated template and the 3D Primer sets are short DNA sequences of 18-22 base pairs, that mesh reconstructed from the depth data (target mesh) at an can be used to verify the presence of a virus, and designed to Abstracts 137

attach to a specific part of a viral DNA. Designing a primer set re- taker�s ability (i.e., their estimated proficiency, such as a quires choosing a region of DNA, avoiding the possibility of hy- knowledge, skill, or personality characteristic). CAT is regularly bridization to a similar sequence, as well as considering its GC used in psychological studies, medical exams, and standard- content and Tm (melting temperature). Coronaviruses, such ized testing to reduce test length and improve measurement as SARS-CoV-2, have a considerably large genome (around 30 accuracy and precision. A key challenge in CAT is item se- thousand nucleotides) when compared to other viruses. With lection. Past algorithms have been designed based on crite- the rapid rise and spread of SARS-CoV-2 variants,it has become ria such as item difficulty and maximum Fisher information. a priory to breach our lack of specif primers available for diag- However, these only consider a fixed-length test, which may nosis of this new variants. Here, we propose an evolutionary- result in it being longer or less precise. To address this problem, based approach to primer design, able to rapidly deliver a high- we formulate a new multi-objective optimization problem to quality primer set for a target sequence of the virus variant. model the trade-off between test length and precision. A binary Starting from viral sequences collected from open repositories, population-based genetic algorithm, NSGA-II, is used to obtain the proposed approach is proven able to uncover a specific the set of Pareto-optimal solutions by maximizing precision primer set for the B.1.1.7 SARS-CoV-2 variant. Only recently and minimizing the number of questions. We evaluate our ap- identified, B.1.1.7 is already considered potentially dangerous, proach with a simulated study using four standard personality as it presents a considerably higher transmissibility when com- assessments. We also investigate the influence of test response pared to other variants. types (e.g., binary versus categorical response) and number of variables (i.e., the number of possible items) on performance. The results obtained show multi-objective optimization can RWA 3 be used in CAT to minimize overall test length and improve Tuesday, July 13, 14:20-15:40 Room RWA measurement precision and overall accuracy.

A Genetic Algorithm Approach to Virtual Topology Design MA-ABC: A Memetic Algorithm Optimizing Attractiveness, for Multi-Layer Communication Networks Balance, and Cost for Capacitated Arc Routing Problems Uwe Bauknecht, Institute of Communication Networks and Muhilan Ramamoorthy, ARIZONA STATE UNIVERSITY, Vio- Computer Engineering (IKR), University of Stuttgart let Syrotiuk, ARIZONA STATE UNIVERSITY, Stephanie Forrest, The core networks of current telecommunication infrastruc- ARIZONA STATE UNIVERSITY tures are typically engineered as multi-layer networks. The Services such as garbage collection, road gritting, street sweep- uppermost layer is defined by the virtual topology, which deter- ing, and power line inspection can each be formulated as a mines the logical connections between core network routers. capacitated arc routing problem (CARP). The traditional formu- This topology is realized by optical paths in the lower layer, lation of CARP has the goal of minimizing the total cost of the which is defined by optical fiber connections between net- routes making up a solution. Recently, operators of such ser- work nodes. Minimizing the hardware cost incurred by these vices require routes that are balanced and visually attractive in optical paths for a given set of traffic demands is a common addition to low cost. Routes that are balanced are about equal combinatorial optimization problem in network planning, of- in length and provide fair work assignments. Visually attractive ten approached by Mixed-Integer Linear Programming. How- routes are subjective, but they usually involve non-crossing ever, increasing network densities and the introduction of ad- routes that provide well defined service areas. These additional ditional constraints will impact tractability of future network features are important because they address operational com- problems. In order to provide a more scalable method, we plexities that arise from using the routes in practice. This paper suggest a Genetic Algorithm-based approach that optimizes presents MA-ABC, a memetic algorithm to find solutions for the virtual topology and subsequently derives the remaining CARP that maximize route attractiveness and balance, while parameters from it. Our genetic encoding utilizes a combi- minimizing total cost. A novel fitness function combines route nation of spanning trees and augmentation links to quickly overlap with route contiguity to assess route attractiveness. MA- form meaningful topologies. We compare the results of our ABC is the first to incorporate attractiveness in a three-objective approach to known linear programming solutions in simple search for heuristic solutions for CARP.Experimental results on scenarios and to a competing heuristic based on Simulated CARP benchmark instances show that MA-ABC finds a diverse Annealing in large-scale problems. set of heuristic solutions at the Pareto front, providing a wide choice for service operators to tradeoff design objectives. Multi-Objective Optimization of Item Selection in Computerized Adaptive Testing Evaluating Medical Aesthetics Treatments through Evolved Dena Mujtaba, Michigan State University, Nihar Mahapatra, Age-Estimation Models Michigan State University Risto Miikkulainen, The University of Texas at Austin, Cog- Computerized-adaptive testing (CAT) is a form of assessment nizant Technology Solutions, Elliot Meyerson, Cognizant Tech- in which items/questions are administered based upon a test nology Solutions, Xin Qiu, Cognizant Technology Solutions, 138 Abstracts

Ujjayant Sinha, Cognizant Technology Solutions, Raghav Ku- segmentation approach. Furthermore, we provide lists of audio mar, Cognizant Technology Solutions, Karen Hofmann, Cog- features from non-dominated fronts which most significantly nizant Technology Solutions, Yiyang Yan, AbbVie, Inc., Michael contribute to the estimation of given boundaries (the first ob- Ye, AbbVie, Inc., Jingyuan Yang, AbbVie, Inc., Damon Caiazza, jective) and most significantly reduce the performance of the AbbVie, Inc., Stephanie Manson Brown, AbbVie, Inc. prediction of other boundaries (the second objective). Estimating a person’s age from a facial image is a challenging problem with clinical applications. Several medical aesthet- Evolutionary Meta Reinforcement Learning for Portfolio ics treatments have been developed that alter the skin texture Optimization and other facial features, with the goal of potentially improv- ing patient’s appearance and perceived age. In this paper, this Myoung Hoon Ha, KAIST, Seung-geun Chi, Seoul National Uni- effect was evaluated using evolutionary neural networks with versity, Sangyeop Lee, Samsung Electronics, Yujin Cha, KAIST, uncertainty estimation. First, a realistic dataset was obtained Byung-Ro Moon, Seoul National University from clinical studies that makes it possible to estimate age Portfolio optimization is a control problem whose objective more reliably than e.g. datasets of celebrity images. Second, a is to find the optimal strategy for the process of selecting the neuroevolution approach was developed that customizes the proportions of assets that can provide the maximum return. architecture, learning, and data augmentation hyperparame- Conventional approaches formulate the problem as a single ters and the loss function to this task. Using state-of-the-art Markov decision process and apply reinforcement learning computer vision architectures as a starting point, evolution methods to provide solutions. However, it is well known that improved their original accuracy significantly, eventually out- financial markets involve non-stationary processes, leading to performing the best human optimizations in this task. Third, violations of this assumption in these methods. In this work, we the reliability of the age predictions was estimated using RIO, a reformulate the portfolio optimization problem to deal with the Gaussian-Process-based uncertainty model. Evaluation on a non-stationary nature of financial markets. In our approach, real-world Botox treatment dataset shows that the treatment we divide a long-term process into multiple short-term pro- has a quantifiable result: The patients’ estimated age is reduced cesses to adapt to context changes and consider the portfolio significantly compared to placebo treatments. The study thus optimization problem as a multitask control problem. There- shows how AI can be harnessed in a new role: To provide an after, we propose an evolutionary meta reinforcement learning objective quantitative measure of a subjective perception, in approach to search for an initial policy that can quickly adapt this case the proposed effectiveness of medical aesthetics treat- to the upcoming target tasks. We model the policies as convo- ments. lutional networks that can score the match of the patterns in market data charts. Finally, we test our approach using real- world cryptographic currency data and show that it adapts well RWA 4 to the changes in the market and leads to better profitability. Wednesday, July 14, 10:20-11:40 Room RWA

An Evolutionary Multi-Objective Feature Selection Continuously Running Genetic Algorithm for Real-Time Approach for Detecting Music Segment Boundaries of Networking Device Optimization Specific Types Amit Mandelbaum, Nvidia, Doron Haritan, Nvidia, Natali Igor Vatolkin, TU Dortmund, Fabian Ostermann, TU Dort- Shechtman, Nvidia mund, Meinard Müller, Audiolabs Erlangen Networking devices deployed in ultra-scale data centers must The goal of music segmentation is to identify boundaries be- run perfectly and in real-time. The networking device perfor- tween parts of music pieces which are perceived as entities. mance is tuned using the device configuration registers. The Segment boundaries often go along with a change in musical optimal configuration is derived from the network topology properties including instrumentation, key, and tempo (or a and traffic patterns. As a result, it is not possible to specify a combination thereof). One can consider different types (or single configuration that fits all scenarios, and manual tuning classes) of boundaries according to these musical properties. is required in order to optimize the devices’ performance. Such In contrast to existing datasets with missing specifications of tuning slows down data center deployments and consumes changing properties for annotated boundaries, we have cre- massive resources. Moreover, as traffic patterns change, the ated a set of artificial music tracks with precise annotations original tuning becomes obsolete and causes degraded perfor- for boundaries of different types. This allows for a profound mance. This necessitates expensive re-tuning, which in some analysis and interpretation of annotated and predicted bound- cases is infeasible. In this work, we present ZTT: a continu- aries and a more exhaustive comparison of different segmenta- ously running Genetic Algorithm that can be used for online, tion algorithms. For this scenario, we formulate a novel multi- automatic tuning of the networking device parameters. ZTT objective optimisation task that identifies boundaries of only a is adaptive, fast to respond and have low computational costs specific type. The optimisation is conducted by means of evo- required for running on a networking device. We test ZTT in a lutionary multi-objective feature selection and a novelty-based diversity of real-world traffic scenarios and show that it is able Abstracts 139

to obtain a significant performance boost over static configu- the reinforcement learning (RL) technology. The benefit is that rations suggested by experts. We also demonstrate that ZTT is we no longer need to re-execute the GA for each new request able to outperform alternative search algorithms like Simulated arrived. Instead, the approach keeps the historical solutions Annealing and Recursive Random Search even when those are in track and maintains a RL agent to sequentially decide how adapted to better match the task at hand. to handle each new request. Experimental results show that the proposed approach is able to stably provide high-quality Multi-objective Optimization Across Multiple Concepts: A solutions, while greatly reducing the average time overhead of Case Study on Lattice Structure Design the web server. Brandon Parker, University of New South Wales, Australian De- fence Force, Hemant Singh, University of New South Wales, A Genetic Algorithm For AC Optimal Transmission Tapabrata Ray, University of New South Wales Switching Evolutionary multi-objective optimization (EMO) is often used Masood Jabarnejad, Dogus University to deal with practical problems in engineering design to iden- Optimal transmission switching (OTS) is a new practice in tify products that exhibit the best possible compromise be- power systems and can improve the economics of electric tween multiple conflicting performance criteria. Much of the power systems integrated with renewable resources such as literature in EMO considers algorithmic developments and wind. In OTS modeling binary decision variables are added to benchmarking problems involving a single concept only. How- the optimal power flow (OPF) problem to represent on and off ever, in practice, there could be many applications where the switching status of lines. This extension to alternative current solution of a given problem may be represented using multiple optimal power flow (ACOPF) problem results in a mixed inte- concepts, and optimizing each one of them individually to ob- ger nonlinear program (MINLP) which is not guaranteed to be tain the overall Pareto front may be inefficient. To address this solved optimally by existing solution methods and also requires gap, in this study, we firstly develop computer-aided models of excessive computation times for large real systems. In this pa- multiple concepts for a simulation-based problem which can per we develop a genetic algorithm (GA) for ACOPF based OTS serve as a good application of multi-concept optimization. The problem. In our GA approach we benefit from the structure of problem involves the design of lattice structures with different power transmission network and develop a line scoring method types of unit cells (each constituting a concept), which can and a graphical distance based local improvement technique be customized to suit a range of real-world applications such to better search the solution space. We compare our proposed as design of structures with minimal thermal expansion, low genetic algorithm with two greedy heuristics on test power sys- weight and high rigidity etc. Furthermore, we develop baseline tems with renewable resources of energy. The results show evolutionary strategies to search across multiple concepts si- that our proposed approach finds more economic solutions multaneously. Empirical experiments show that the presented especially in larger power systems. strategies are able to outperform conventional single concept- based approach. Moreover, some challenges are also uncovered Heuristic Strategies for Solving Complex Interacting which prompts the need for further developments. Stockpile Blending Problem with Chance Constraints Yue Xie, The University of Adelaide, Aneta Neumann, The Uni- versity of Adelaide, Frank Neumann, The University of Adelaide RWA 5 The stockpile blending problem seeks to determine how many Wednesday, July 14, 12:20-13:40 Room RWA tonnages of ore that stockpiles provide and to which parcels. This scheduling model maximizes the volume of valuable ma- An Efficient Computational Approach for Automatic terial in the final production subject to resource capacities, Itinerary Planning on Web Servers constraints for mill machines, and customer requirements. Mo- Zeyuan Ma, South China University of Technology, Hong- tivated by the uncertainty in the geologic input date which af- shu Guo, South China University of Technology, Yinxuan Gui, fects optimization, we consider the stockpile blending problem South China University of Technology, Yue-Jiao Gong, South with uncertainty in material grades. A non-linear continuous China University of Technology optimization model developed here to integrate uncertain vari- The automatic itinerary planning service requires to gener- ables to optimization. We introduce chance constraints that ate multiple-day schedules automatically under user-specified are used to guarantee the constraint is violated with a small POIs and constraints. Knowing as an NP-Hard optimization probability to tackle the stochastic material grades. We inves- problem, the task is commonly solved by (meta-)heuristic al- tigate a well-known approach in this paper, which is used to gorithms such as the genetic algorithms (GAs). However, con- solve optimization problems over continuous space, namely sidering the concurrent requests received by a web server in the differential evolution (DE) algorithm. In the experiment practice, the time efficiency of the existing itinerary planners section, we compare the performance of the algorithm with the can be rather unsatisfactory. To address the issue, this paper deterministic model and three chance constraint models by us- proposes a computational approach that hybridizing a GA with ing a synthetic benchmark. We also evaluate the effectiveness 140 Abstracts

of different chance constraints. to find an optimized production schedule for the painting of car parts. In this paper, we propose a memetic algorithm to solve Solving the Paintshop Scheduling Problem with Memetic this problem. An initial population is generated, followed by Algorithms the constant evolution of generations. Selection, crossover, mu- Wolfgang Weintritt, TU Wien, Nysret Musliu, TU Wien, Felix tation, and local improvement operators are applied in each Winter, TU Wien generation. We design three novel crossover operators that Finding efficient production schedules for automotive paint consider problem-specific knowledge. Finally, we carefully shops is a challenging task and several paint shop problem configure our algorithm, including automated and manual pa- variants have been investigated in the past. In this work we rameter tuning. Using a set of available real-life benchmark focus on a recently introduced real-life paint shop scheduling instances from the literature, we perform an extensive experi- problem appearing in the automotive supply industry where mental evaluation of our algorithm. The experimental results car parts, which need to be painted, are placed upon carrier de- show that our memetic algorithm yields competitive results vices. These carriers are placed on a conveyor belt and moved for small- and medium-sized instances and is able to set new into painting cabins, where robots apply the paint. The aim is upper bounds for some of the problem instances.

Search-Based Software Engineering

SBSE 1 propagation of updates between two models, is an important problem in the area of model-driven engineering (MDE). Com- Wednesday, July 14, 10:20-11:40 Room SBSE pared to other consistency management tasks, synchronising concurrent updates is especially challenging as they can be Encoding the Certainty of Boolean Variables to Improve the conflicting, such that restoring a consistent state is not possi- Guidance for Search-Based Test Generation ble when all updates must be considered. Recent approaches Sebastian Vogl, University of Passau, Sebastian Schweikl, Uni- create a search space of possible solutions and determine the versity of Passau, Gordon Fraser, University of Passau optimum solution via exact methods, such as integer linear Search-based test generation commonly uses fitness functions programming (ILP), via a configurable, scalarised objective based on branch distances, i.e., estimations of how close con- function that takes conflicting goals into account. However, ditional statements in a program are to evaluating to true or to the determination of suitable configuration parameters and false. When conditional statements depend on Boolean vari- runtime efficiency improvements are still an open issue, which ables or Boolean-valued methods, the branch distance metric is is commonly addressed by using heuristics instead of exact unable to provide any guidance to the search, causing challeng- methods. We investigate on whether it is beneficial to apply ing plateaus in the fitness landscape. A commonly proposed heuristics to solve concurrent model synchronisation prob- solution is to apply testability transformations, which trans- lems. First, a multiobjective evolutionary algorithm is used for form the program in a way that avoids conditional statements small instances for which all pareto-optimal solutions can be from depending on Boolean values. In this paper we introduce presented to a user to select the best one. Second, for larger the concept of Certainty Booleans, which encode how certain models, we propose a method to determine suitable weight- a true or false Boolean value is. Using these Certainty Booleans, ings for aggregating all objectives into a single function. Finally, a basic testability transformation allows to restore gradients in these insights are used to recommend a strategy for determin- the fitness landscape for Boolean branches, even when Boolean ing solutions of satisfying quality within an acceptable amount values are the result of complex interprocedural calculations. of time. Evaluation on a set of complex Java classes and the EvoSuite test generator shows that this testability transformation sub- Analyzing the Impact of Product Configuration Variations stantially alters the fitness landscape for Boolean branches, and on Advanced Driver Assistance Systems with Search the altered fitness landscape leads to performance improve- ments. However, Boolean branches turn out to be much rarer Kaiou Yin, Nanjing University of Aeronautics and Astronautics, than anticipated, such that the overall effects on code coverage Paolo Arcaini, National Institute of Informatics, Tao Yue, Nan- are minimal. jing University of Aeronautics and Astronautics, Shaukat Ali, Simula Research Laboratory Due to the complexity of designing vehicle products and the Concurrent Model Synchronisation with Multiple inherent uncertainties in their operating environments, en- Objectives suring the safety of their Advanced Driver Assistance Systems Nils Weidmann, University of Paderborn, Gregor Engels, Uni- (ADASs) becomes crucial. Especially, very minor changes to a versity of Paderborn vehicle design, for instance, due to production errors or com- Concurrent model synchronisation, i.e. the (bidirectional) ponent degradation, might lead to failures of ADASs and, there- Abstracts 141

fore, catastrophic consequences such as collision occurrences. proach with NSGA-II significantly outperforms the random Motivated by this, we propose a multi-objective search-based search. Moreover, based on the detailed analyses of the results, approach (employing NSGA-II) to find minimum changes to we identify some parameters for which minor changes to their the configuration of a set of configurable parameters of a ve- values lead the vehicle into collisions, and demonstrated the hicle design, such that the collision probability is maximized, importance of studying the configuration of multiple parame- consequently leading to a reversal change in its safety. We con- ters in a single search and the impact of their interactions on ducted experiments, in a vehicle driving simulator, to evaluate causing collisions. the effectiveness of our approach. Results show that our ap-

Theory

ACO-SI 3 + ENUM 1 + THEORY 1 - BP all parameter values in each iteration randomly from a suitably scaled power-law distribution. We demonstrate the effective- Tuesday, July 13, 14:20-15:40 Rooms ACO-SI, ENUM, ness of this approach via runtime analyses of the (1 (λ,λ)) THEORY + genetic algorithm with all three parameters chosen in this man- Self-Adjusting Population Sizes for Non-Elitist Evolutionary ner. We show that this algorithm on the one hand can imitate simple hill-climbers like the (1 1) EA, giving the same asymp- Algorithms: Why Success Rates Matter + totic runtime on some simple problems. On the other hand, Mario Hevia Fajardo, The University of Sheffield, Dirk Sudholt, this algorithm is also very efficient on jump functions, where University of Passau the best static parameters are very different from those neces- Recent theoretical studies have shown that self-adjusting mech- sary to optimize simple problems. We prove a performance anisms can provably outperform the best static parameters in guarantee that is comparable to, and sometimes even better evolutionary algorithms on discrete problems. However, the than, the best performance known for static parameters. We majority of these studies concerned elitist algorithms and we complement our theoretical results with a rigorous empirical do not have a clear answer on whether the same mechanisms study confirming what the asymptotic runtime results suggest. can be applied for non-elitist algorithms. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size λ in the THEORY 2 non-elitist (1, λ) EA. It is known that the (1, λ) EA has a sharp Wednesday, July 14, 10:20-11:40 Room THEORY threshold with respect to the choice of λ where the runtime on OneMax changes from polynomial to exponential time. Hence, More Precise Runtime Analyses of Non-elitist EAs in it is not clear whether parameter control mechanisms are able Uncertain Environments to find and maintain suitable values of λ. We show that the an- Per Lehre, University of Birmingham, Xiaoyu Qin, University of swer crucially depends on the success rate s (i.,e. a one-(s+1)-th Birmingham success rule). We prove that, if the success rate is appropriately Real-world optimisation problems often involve uncertainties. small, the self-adjusting (1, λ) EA optimises OneMax in O(n) In the past decade, several rigorous analysis results for evolu- expected generations and O(n log n) expected evaluations. A tionary algorithms (EAs) on discrete problems show that EAs small success rate is crucial: we also show that if the success can cope with low-level uncertainties, and sometimes benefit rate is too large, the algorithm has an exponential runtime on from uncertainties. Using non-elitist EAs with large popula- OneMax. tion size is a promising approach to handle higher levels of uncertainties. However, the performance of non-elitist EAs in Lazy Parameter Tuning and Control: Choosing All some common fitness-uncertainty scenarios is still unknown. Parameters Randomly From a Power-Law Distribution We analyse the runtime of non-elitist EAs on ONEMAX and Denis Antipov, ITMO University, Maxim Buzdalov, ITMO Uni- LEADINGONES under prior and posterior noise models, and the versity, Benjamin Doerr, Ecole Polytechnique, Laboratoire dynamic binary value problem (DYNBV). Our analyses are more d’Informatique (LIX) extensive and precise than previous analyses of non-elitist EAs. Most evolutionary algorithms have multiple parameters and In several settings, we prove that the non-elitist EAs beat the their values drastically affect the performance. Due to the often current state of the art results. Previous work shows that the complicated interplay of the parameters, setting these values population size and mutation rate can dramatically impact the right for a particular problem is a challenging task. This task performance of non-elitist EAs. The optimal choices of these becomes even more complicated when the optimal parame- parameters depend on the level of uncertainties in the fitness ter values change significantly during the run of the algorithm functions. We provide more precise guidance on how to choose since then a dynamic parameter choice is necessary. In this mutation rate and population size as a function of the level of work, we propose a lazy but effective solution, namely choosing uncertainties. 142 Abstracts

Stagnation Detection in Highly Multimodal Fitness of fitness levels by Wegener. It uses a partition of the search Landscapes space into a sequence of levels which are traversed by the al- Amirhossein Rajabi, Technical University of Denmark, Carsten gorithm in increasing order, possibly skipping levels. An easy, Witt, Technical University of Denmark but often strong upper bound for the run time can then be de- rived by adding the reciprocals of the probabilities to leave the Stagnation detection has been proposed as a mechanism for levels (or upper bounds for these). Unfortunately, a similarly randomized search heuristics to escape from local optima by effective method for proving lower bounds has not yet been automatically increasing the size of the neighborhood to find established. The strongest such method, proposed by Sudholt the so-called gap size, i.e., the distance to the next improve- (2013), requires a careful choice of the viscosity parameters γ , ment. Its usefulness has mostly been considered in simple i,j 0 i j n. In this paper we present two new variants of the multimodal landscapes with few local optima that could be ≤ < ≤ method, one for upper and one for lower bounds. Besides the crossed one after another. In multimodal landscapes with a level leaving probabilities, they only rely on the probabilities more complex location of optima of similar gap size, stagnation that levels are visited at all. We show that these can be com- detection suffers from the fact that the neighborhood size is puted or estimated without greater difficulties and apply our frequently reset to 1 without using gap sizes that were promis- method to reprove, in an easy and natural way, known results ing in the past. In this paper, we investigate a new mechanism for the expected run time of the 1+1 EA on (i) LeadingOnes, (ii) called radius memory which can be added to stagnation de- OneMax and (iii) long paths. tection to control the search radius more carefully by giving preference to values that were successful in the past. We im- m plement this idea in an algorithm called SD-RLS and show THEORY 3 compared to previous variants of stagnation detection that it yields speed-ups for linear functions under uniform constraints Wednesday, July 14, 12:20-13:40 Room THEORY and the minimum spanning tree. Moreover, its running time Non-elitist Evolutionary Algorithms excel in Fitness does not significantly deteriorate on unimodal functions and a Landscapes with Sparse Deceptive Regions and Dense generalization of the Jump benchmark. Finally, we present ex- Valleys perimental results carried out to study SD-RLSm and compare it with other algorithms. Duc-Cuong Dang, University of Southampton, Anton Eremeev, Sobolev Institute of Mathematics SB RAS, INION RAN, Per Runtime Analysis of RLS and the (1+1) EA for the Lehre, University of Birmingham Chance-constrained Knapsack Problem with Correlated It is largely unknown how the runtime of evolutionary algo- Uniform Weights rithms depends on fitness landscape characteristics for broad Yue Xie, The University of Adelaide, Aneta Neumann, The Uni- classes of problems. Runtime guarantees for complex and versity of Adelaide, Frank Neumann, The University of Ade- multi-modal problems where EAs are typically applied are laide, Andrew Sutton, University of Minnesota Duluth rarely available. We present a parameterised problem class SparseLocalOpt, where the class with parameters α,² [0,1] Addressing a complex real-world optimization problem is a ∈ challenging task. The chance-constrained knapsack problem contains all fitness landscapes with deceptive regions of spar- with correlated uniform weights plays an important role in sity ε and fitness valleys of density α. We study how the runtime the case where dependent stochastic components are consid- of EAs depends on these fitness landscape parameters. We find that for any constant density and sparsity α,ε (0,1), Sparse- ered. We perform runtime analysis of a randomized search ∈ LocalOpt has exponential elitist (µ λ) black-box complexity, algorithm (RSA) and a basic evolutionary algorithm (EA) for + the chance-constrained knapsack problem with correlated uni- implying that a wide range of elitist EAs fail even for mildly form weights. We prove bounds for both algorithms for pro- deceptive and multi-modal landscapes. In contrast, we derive ducing a feasible solution. Furthermore, we investigate the a set of sufficient conditions for non-elitist EAs to optimise any behaviour of the algorithms and carry out analyses on two set- problem in SparseLocalOpt in expected polynomial time for tings: uniform profit value and the setting in which every group broad values of α and ε. These conditions can be satisfied for shares an arbitrary profit profile. We provide insight into the tournament selection and linear ranking selection, but not for structure of these problems and show how the weight corre- (µ,λ)-selection. lations and the different profit profiles influence the runtime behavior of both algorithms in the chance-constrained setting. Generalized Jump Functions Henry Bambury, École Polytechnique, Antoine Bultel, École Lower Bounds from Fitness Levels Made Easy Polytechnique, Benjamin Doerr, École Polytechnique, Labora- Benjamin Doerr, Ecole Polytechnique, Laboratoire toire d’Informatique (LIX) d’Informatique (LIX), Timo Kötzing, Hasso Plattner Institute Jump functions are the most studied non-unimodal bench- One of the first and easy to use techniques for proving run time mark in the theory of evolutionary algorithms (EAs). They have bounds for evolutionary algorithms is the so-called method significantly improved our understanding of how EAs escape Abstracts 143

from local optima. However, their particular structure –to leave Fukuchi, University of Tsukuba, RIKEN AIP, Jun Sakuma, Uni- the local optimum the EA can only jump directly to the global versity of Tsukuba, RIKEN AIP, Youhei Akimoto, University of optimum– raises the question of how representative the recent Tsukuba, RIKEN AIP findings are. For this reason, we propose an extended class JUMPk,δ of jump functions that incorporate a valley of low fit- The (1+1)-evolution strategy (ES) with success-based step-size ness of width δ starting at distance k from the global optimum. adaptation is analyzed on a general convex quadratic func- We prove that several previous results extend to this more gen- tion and its monotone transformation, that is, f (x) g((x = − eral class: for all k o(n1/3) and δ k, the optimal mutation x )H (x x )), where g : R R is a strictly increasing func- = < ∗ − ∗ → rate for the (1 1) EA is δ , and the fast (1 1) EA runs faster tion, H is a positive-definite symmetric matrix, and x Rd + n + ∗ ∈ than the classical (1 1) EA by a factor super-exponential in is the optimal solution of f . The convergence rate, that is, the + δ. However, we also observe that some known results do not decrease rate of the distance from a search point mt to the generalize: the randomized local search algorithm with stagna- optimal solution x , is proven to be in O(exp( L/(H))), where ∗ − tion detection, which is faster than the fast (1 1) EA by a factor L is the smallest eigenvalue of H and (H) is the trace of H. + polynomial in k on JUMP , is slower by a factor polynomial in This result generalizes the known rate of O(exp( 1/d)) for the k − n on some JUMP instances. Computationally, the new class case of H I (I is the identity matrix of dimension d) and k,δ = d d allows experiments with wider fitness valleys, especially when O(exp( 1/(d ξ))) for the case of H (ξ I , I ). To the best − · = · d/2 d/2 they lie further away from the global optimum. of our knowledge, this is the first study in which the conver- gence rate of the (1+1)-ES is derived explicitly and rigorously on Convergence Rate of the (1+1)-Evolution Strategy with a general convex quadratic function, which depicts the impact Success-Based Step-Size Adaptation on Convex Quadratic of the distribution of the eigenvalues in the Hessian H on the Function optimization and not only the impact of the condition number Daiki Morinaga, University of Tsukuba, RIKEN AIP, Kazuto of H.

Author Index

Swiechowski,´ Maciej, 93 Aranha, Claus, 95 Ðurasevi´c,Marko, 54 Arcaini, Paolo, 80, 82, 89, 128, 130, 140 Çalik, Hatice, 44 Archambault, Daniel, 57 Cerný,ˇ Vojtˇech, 100 Archetti, Francesco, 99 Šenkeˇrík,Roman, 51, 67 Ardagna, Danilo, 55 Škvorc, Urban, 93 Ascheid, Gerd, 95 Asilian Bidgoli, Azam, 49 Abd Elaziz, Mohamed, 47 Assimi, Hirad, 98 Abdelatti, Marwan, 47, 99 Assion, Felix, 95 Abdullahi, Amina, 95 Asteroth, Alexander, 77, 85, 124 Acampora, Giovanni, 99 Aubert-Kato, Nathanael, 94 Acosta-Mesa, Héctor-Gabriel, 55 Audemard, Gilles, 82, 123 Affenzeller, Michael, 44, 51 Auger, Anne, 38, 46 Agarwal, Pulak, 49 Augusto, Douglas, 50 Agarwal, Sumeet, 96 Aydeniz, Ayhan, 94 Aggarwal, Vaneet, 86, 134 Aydo˘gan, Reyhan, 44 Aggoune-Matalaa, Wassila, 57 Azad, R. Muhammad Atif, 97 Ajimakin, Adetunji, 95 Azimlu, Fateme, 53 Akimoto, Youhei, 40, 77, 80, 81, 90, 91, 118, 123, 135, Aziz-Alaoui, Amine, 47 143 Azzimonti, Dario, 51 Akman, Ozgur, 46 Aldeia, Guilherme, 86, 126 Bäck, Thomas, 40, 44, 47, 55, 56, 68, 77, 85, 90, 97, 118, Alderliesten, Tanja, 77, 85, 120 124 Aleksandrova, Marharyta, 57 Böther, Maximilian, 77, 81, 135 Aleti, Aldeida, 98 Baeta, Francisco, 88, 127 Alexander, Bradley, 39 Baffier, Jean-François, 95 Ali, Shaukat, 82, 89, 130, 140 Bahsoon, Rami, 99 Alkilabi, Muhanad, 84, 105 Baier, Hendrik, 48 Allmendinger, Richard, 52, 53 Baioletti, Marco, 45 Almeida, Renuá, 96 Bambury, Henry, 91, 142 Alofi, Akram, 99 Banzhaf, Wolfgang, 100 Alza, Joan, 45 Barajas, Samantha, 67 Amaral, Ryan, 87, 106 Baral, Avital, 82, 123 André, Jean-Baptiste, 99 Barbosa, Helio, 50 Ansari Ardeh, Mazhar, 88, 127 Barr, Valerie, 57 Antipov, Denis, 49, 87, 141 Bartlett, Mark, 45 Anwar, Mohd, 86, 113 Bartoli, Alberto, 76, 81, 106 Apeldoorn, Daan, 69 Bartz-Beielstein, Thomas, 51, 55, 68, 97, 99 Aquirre, Hernan, 82, 120 Basterrech, Sebastian, 96 Aranda, Ramón, 67 Bastos-Filho, Carmelo, 83, 104 146 AUTHOR INDEX

Bauknecht, Uwe, 88, 137 Brockhoff, Dimo, 39, 46 Bayer, Caleidgh, 87, 106 Browne, Will, 40, 52 Bayrak, Ahmet Tu˘grul, 44 Brownlee, Alexander, 39 Beauthier, Charlotte, 99 Brust, Matthias, 98 Becker, Kory, 50 Bultel, Antoine, 91, 142 Beham, Andreas, 46, 68 Burak, Jan, 44 Belardinelli, Francesco, 48 Buzdalov, Maxim, 80, 87, 97, 122, 141 Benavides, Alexander, 87, 112 Byrski, Aleksander, 50, 57 Benavides, Xabier, 45 Benavoli, Alessio, 51 Cagnoni, Stefano, 40, 48 Benbaki, Riade, 76, 87, 105 Cai, Xuebing, 97 Benecki, Pawel, 95 Caiazza, Damon, 88, 138 Bennamoun, Mohammed, 96 Calderon, Gonzalo, 44 Benomar, Ziyad, 76, 87, 105 Candelieri, Antonio, 99 Bensayah, Abdellah, 99 Canizes, Bruno, 68 Benson, Steve, 55 Canonne, Lorenzo, 76, 83, 110 Bergenti, Federico, 48 Capps, Benjamin, 87, 107 Berns, Sebastian, 77, 85, 124 Caraffini, Fabio, 55 Berny, Arnaud, 94, 97 Cardoso, Rui, 80, 133 Bersini, Hugues, 44 Carlet, Claude, 92, 122 Beyer, Hans-Georg, 77, 87, 118 Carletti, Timotéo, 99 Bi, Ying, 96 Carvalho, Samuel, 44 Biazzi, Renata, 99 Castelli, Mauro, 86, 126 Bischl, Bernd, 97 Cazenille, Leo, 89, 94, 108 Bishop, Jordan, 52 Ceberio, Josu, 45 Blank, Julian, 39, 83, 124 Cha, Yujin, 90, 138 Bleidorn, Dominik, 44 Challenor, Peter, 56 Bliek, Laurens, 51, 67 Chan, Jeffrey, 84, 116 Blocho, Miroslaw, 100 Chan-Ley, Mariana, 47 Blum, Christian, 49 Chandrasekaran, Sowmya, 68 Bockholt, Mirco, 94 Chen, Huanhuan, 80, 92, 125, 128 Boisbunon, Aurelie, 86, 126 Chen, Kexin, 98 Bokhari, Mahmoud, 99 Chen, Kuan-Wen, 86, 134 Boks, Rick, 54 Chen, Long, 97 Born, Thorsten, 95 Chen, Qi, 98 Bosman, Anna, 45 Chen, Wei-Neng, 93 Bosman, Peter, 38, 45, 46, 77, 85, 99, 120 Chen, Weiyu, 76, 88, 116 Bossek, Jakob, 45, 81, 85, 109, 110, 121 Chen, Yan, 45 Bossens, David, 98 Cheng, Peng, 94 Boswell, Sharon, 53 Cheriet, Abdelhakim, 99 Bouamama, Salim, 49 Chi, Seung-geun, 90, 138 Boulesnane, Abdennour, 99 Chiba, Kazuhisa, 53 Boumaza, Amine, 99 Chicano, Francisco, 81, 82, 110, 120 Boussaid, Farid, 96 Chitty, Darren, 48, 99 Bouvier, Benjamin, 50 Chlebik, Jakub, 95 Bouvry, Pascal, 98 Cho, Dong-Hee, 57, 94 Bouziri, Hend, 57 Choi, Tae Jong, 91, 119 Boyaci, Burak, 53 Christaki, Kyriaki, 85, 136 Brévilliers, Mathieu, 68 Christie, Lee, 98 Branke, Juergen, 53, 54, 98 Chugh, Tinkle, 52, 53, 56 Bredeche, Nicolas, 99 Claassen, Eric, 85, 136 AUTHOR INDEX 147

Clark, David, 45 Derbel, Bilel, 76, 83, 110, 111 Clay, Sylvain, 49 Derbinsky, Nate, 56 Cleghorn, Christopher, 38, 45 Desell, Travis, 50, 55 Clune, Jeff, 48 Deshpande, Niranjana, 57 Coello Coello, Carlos, 39, 54 Deslippe, Jack, 50 Coletti, Mark, 50, 95 Devi, V. Susheela, 95 Collet, Isabelle, 57 Dias, Douglas, 44 Collet, Pierre, 96 Dias, Duarte, 57 Colton, Simon, 77, 85, 124 Dias, Pedro, 50 Coninx, Alexandre, 47, 48, 76, 81, 89, 101, 106, 108 Diaz, Juan, 80, 129 Connor, Mark, 99 Diggelen, Fuda, 95 Cook, Joshua, 94 Ding, Li, 55 Corbett, Helen, 100 Dinh, Quoc Trung, 83, 104 Correia, João, 88, 97, 127 Divina, Federico, 99 Corus, Dogan, 81, 129 Dixit, Gaurav, 96 Costa, Ernesto, 95 do Rego, Alexander, 47 Costa, Victor, 97 Do, Anh, 77, 85, 120 Cruz-Zelante, Francisco, 95 Do, Duc Dong, 83, 104 Cully, Antoine, 39, 47, 48, 77, 80, 85, 89, 107, 133, 134 Dobos, Daniel, 100 Custode, Leonardo, 53, 84, 92, 109, 132 Dockhorn, Alexander, 69 Cybula, Piotr, 95 Dodda, Mohan, 49 Czajkowski, Marcin, 85, 100, 136 Doerr, Benjamin, 38, 76, 84, 87, 90, 91, 105, 131, 132, 141, 142 Džeroski, Sašo, 98 Doerr, Carola, 47, 53, 68, 80, 92, 97, 98, 122, 123, 125 Daeden, Jonathan, 86, 126 Doncieux, Stephane, 39, 76, 81, 89, 101, 106, 108 Dal Molin, Lara, 87, 107 Dong, Hongbin, 99 Dang, Daniel, 96 Dougherty, Ryan, 68 Dang, Duc-Cuong, 91, 142 Doumanoglou, Alexandros, 67, 85, 136 Danoy, Grégoire, 98 Dröscher, Marcel, 99 Dantas, Augusto, 47 Drakoulis, Petros, 85, 136 Dapeng, Chai, 53 Draude, Sabrina, 100 Daras, Petros, 67, 85, 136 Drechsler, Rolf, 94 Dartmann, Guido, 95 Dreo, Johann, 47, 50 Das, Souvik, 86, 134 Driessel, Tobias, 93 Dasariraju, Satvik, 56 Du, Ke-Jing, 98 De Ath, George, 51, 82, 130 Duarte, Grasiele, 48 De Carlo, Matteo, 100 Dubey, Rahul, 56, 96 De Falco, Ivanoe, 55 Dufek, Amanda, 50 de Franca, Fabricio, 82, 86, 119, 126 Dufossé, Paul, 90, 119 De Jong, Kenneth, 50, 55, 98 Duro, Joao, 50 De Lorenzo, Andrea, 52 Dushatskiy, Arkadiy, 77, 85, 120 De Masi, Giulia, 84, 105 Dziurzanski, Piotr, 82, 131 de Nobel, Jacob, 47, 90, 118 de Weerdt, Mathijs, 51 Ecoffet, Paul, 99 Deb, Kalyanmoy, 39, 83, 94, 124 Eftimov, Tome, 39, 53, 67, 80, 84, 92, 93, 98, 123, 125, Delaney, Gary, 92, 108 131 Delgado, Myriam, 47 Eiben, A. E. (Gusz), 54, 55, 93, 95, 97, 100 Della Cioppa, Antonio, 55 Eisler, Nils, 55 Demir, Kaan, 100 El Krari, Mehdi, 97 Deng, Jiahui, 97 El Yafrani, Mohamed, 47 Depolli, Matjaž, 67 Ellers, Jacintha, 100 148 AUTHOR INDEX

Elsayed, Saber, 53 Furman, Gregory, 95 Emanuello, John, 56 Emmerich, Michael, 52, 55 Gür, Alpar, 99 Enenkel, Jannik, 98 Gómez Vela, Francisco, 99 Engel, Thomas, 57 Gómez-Flores, Wilfrido, 82, 89, 114, 130 Engelbrecht, Andries, 38 Gaier, Adam, 47, 48 Engell, Sebastian, 44 Galanos, Theodoros, 94 Engels, Gregor, 89, 140 Gallagher, Marcus, 52 Epitropakis, Michael G., 68 Galvez, Akemi, 98 Eremeev, Anton, 82, 91, 131, 142 Gandomi, Amir, 100 Ernst, Andreas, 49 Gandra, Vinicius, 44 Evans, Ben, 97 Gao, Fenghui, 97 Everson, Richard, 51, 82, 130 Gao, Shang, 95 Evrard, Fabien, 100 Gao, Zhenyu, 93 Garanovich, Ivan, 53 Fabbro, Rhodri, 57 García Torres, Miguel, 99 Falcón-Cardona, Jesús, 77, 88, 116 García-Nájera, Abel, 77, 88, 116 Fan, Qinglan, 96 Garciarena, Unai, 98 Fanara, Carlo, 86, 126 Garssen, Johan, 85, 136 Faustmann, Georg, 94 Garza-Fabre, Mario, 82, 130 Fellers, Justin, 99 Gaudl, Swen, 46 Feng, Ben, 96 Gautier, Thierry, 50 Fernandez Alzueta, Silvino, 44 Gemrot, Jakub, 100 Ferrante, Eliseo, 84, 95, 100, 105 Gharote, Mangesh, 52 Ferretti, Claudio, 56 Giacobini, Mario, 82, 129 Feutrier, Thomas, 45 Fieldsend, Jonathan, 46, 51, 68, 82, 97, 130 Gibson, Finley, 57 Filippova, Anna, 53 Gijsbers, Pieter, 97 Fischbeck, Philipp, 77, 81, 135 Gil-Gala, Francisco J., 100 Fischer, Sebastian, 95 Glasmachers, Tobias, 46 Fister, Iztok, 98 Gmys, Jan, 50 Fitzgerald, Seth, 92, 108 Gondro, Cedric, 100 Flogard, Eirik, 100 Gong, Yue-Jiao, 91, 139 Floreano, Dario, 92, 109 Gong, Ziyi, 98 Flynn, Colin, 96 González León, Borja, 48 Fontbonne, Nicolas, 99 Gonzalez, Santiago, 86, 113 Forrest, Stephanie, 88, 137 Gottipati, Srivathsa, 93 Foss, Fredrik, 93 Gottschlich, Justin, 50 Fraga, Paulo, 86, 135 Grabowski, Tim, 80, 134 Franzin, Alberto, 44 Gravenor, Michael, 57 Fraser, Gordon, 89, 140 Grbic, Djordje, 68 Freitas, Alan, 49 Grenet, Ingrid, 86, 126 Frej, Bartosz, 45, 92, 121 Grigorev, Georgii, 57 French, Tim, 92, 121 Gu, Shi, 95 Friedrich, Tobias, 77, 81, 135 Guckert, Michael, 98 Frisco, Pierluigi, 44 Gui, Yinxuan, 91, 139 Fujimoto, Noriyuki, 48 Guibadj, Rym, 97 Fujita, André, 99 Guijt, Arthur, 51, 67 Fukuchi, Kazuto, 77, 80, 81, 91, 123, 135, 143 Guo, Hongshu, 91, 139 Fukumoto, Yukiko, 97 Guo, Mingyu, 77, 85, 120 Fukunaga, Alex, 46 Gurrapadi, Nishant, 83, 104 AUTHOR INDEX 149

Hähner, Jörg, 54 Homaifar, Abdollah, 86, 93, 113 Härmä, Aki, 98 Horiuchi, Motoki, 89, 114 Hüllermeier, Eyke, 89, 115 Hort, Max, 100 Hüttenrauch, Maximilian, 48 Howard, David, 92, 108 Hà, Minh Hoàng, 83, 104 Hu, Ting, 94, 99 Ha, Huong, 84, 116 Hu, Xiao-Min, 93 Ha, Myoung Hoon, 90, 138 Huang, Han, 67 Haas, Quentin, 95 Huang, Jianbin, 97 Hadidi, Lars, 69 Hvatov, Alexander, 57 Hagemann, Tanja, 95 Hagg, Alexander, 77, 85, 124 Iacca, Giovanni, 53, 54, 84, 92, 95, 109, 132 Hakanen, Jussi, 52, 53 Ianta, Alexandru, 68, 87, 106 Hallawa, Ahmed, 95 Ibarra-Vazquez, Gerardo, 47 Hamacher, Kay, 76, 83, 111 Idoumghar, Lhassane, 68 Han, Kate, 98 Iglesias, Andres, 98 Han, Nan, 97 Imani, Farhad, 47 Handl, Julia, 82, 130 Imrie, Calum, 84, 105 Hanghofer, Roland, 44 Indramohan, Vivek Padmanaabhan, 97 Hansen, Nikolaus, 38, 40, 46, 90, 91, 117, 119 Indrusiak, Leandro, 82, 131 Hansmeier, Tim, 52 Irazusta Garmendia, Andoni, 45 Hanuš, Marek, 90, 118 Irurozki, Ekhiñe, 44, 83, 111 Hao, Zhifeng, 67 Ishibuchi, Hisao, 76, 84, 88, 91, 115–117 Harada, Tomohiro, 80, 122 Ishikawa, Fuyuki, 80, 128 Haraldsson, Saemundur, 39 Ito, Mika, 94 Haritan, Doron, 90, 138 Harris, Sean, 56, 95 Jabarnejad, Masood, 92, 139 Harrison, Kyle, 53 Jahangirova, Gunel, 84, 132 Hart, Emma, 48, 54, 80, 133 Jakobovic, Domagoj, 40, 54, 86, 92, 122, 126 Hauser, Helmut, 96 Jankovic, Anja, 80, 92, 123, 124 Hayamizu, Yohei, 97 Jaros, Jiri, 95 He, Linjun, 84, 91, 115, 117 Jastrzab, Tomasz, 100 Heider, Michael, 52, 54 Jaszkiewicz, Andrzej, 84, 95, 115 Hein, Daniel, 44 Jeannin-Girardon, Anne, 96 Helali Moghadam, Mahshid, 51 Jesus, Alexandre, 57, 83, 111 Helbig, Marde, 39 Jethwani, Hetvi, 96 Helmuth, Thomas, 40, 77, 83, 125 Jiang, Hao, 84, 132 Hemberg, Erik, 38, 56, 82, 100, 123 Jiang, Xi, 68 Hendawi, Abdeltawab, 99 Jin, Kezhong, 97 Hendley, Robert, 99 Jinying, Zou, 53 Hernandez Castellanos, Carlos, 93 Johnson, Alexander, 98 Hernando, Leticia, 45 Jordan, Jakob, 96 Herrmann, Michael, 84, 105 José-García, Adán, 82, 89, 114, 130 Hevia Fajardo, Mario, 78, 87, 141 Jourdan, Laetitia, 49, 82, 123 Heywood, Malcolm, 68, 87, 106 Jurczuk, Krzysztof, 85, 100, 136 Hinojosa, Salvador, 51 Hinterleitner, Alexander, 99 Kötzing, Timo, 90, 142 Hiscock, Rebecca, 100 Kadavy, Tomas, 51, 67 Hofer, Bernd, 44 Kadzi´nski, Miłosz, 91, 117 Hofmann, Karen, 88, 138 Kaisers, Michael, 48 Holeˇna, Martin, 90, 118 Kalyuzhnaya, Anna, 57 Holznigenkemper, Jana, 98 Kammerer, Lukas, 95 150 AUTHOR INDEX

Kantor, Daniel, 82, 119 Kulpa, Tomasz, 83, 104 Kanwal, Jasmeen, 87, 107 Kumar, Raghav, 88, 138 Kapelan, Zoran, 100 Kurka, David, 80, 133 Karakottas, Antonis, 67, 85, 136 Karatas, Melike, 46 Lüders, Ricardo, 47 Karder, Johannes, 46, 68 López Serrano, Albert, 49 Karlsson, Rickard, 67 López-Ibáñez, Manuel, 38, 39, 47, 53, 56, 80, 83, 91, Karns, Joshua, 50 111, 117, 129 Kawulok, Michal, 100 La Cava, William, 40 Kazikova, Anezka, 51 Labisch, Daniel, 44 Keedwell, Edward, 97, 100 Lacroix, Benjamin, 76, 88, 116 Kefer, Kathrin, 44 Laflaquière, Alban, 76, 81, 106 Kefer, Patrick, 44 Lan, Gongjin, 93 Kelly, Jonathan, 82, 123 Langdon, William, 45, 82, 96, 130 Kelly, Peter, 77, 83, 125 Langfeld, James, 55 Kelly, Stephen, 100 LaTorre, Antonio, 67 Kerschke, Pascal, 53, 92 Laue, Cheyenne, 86, 126 Kessaci, Marie-Éléonore, 45 Laurent, Thomas, 80, 128 Khadka, Shauharda, 87, 107 Le Goff, Léni, 48 Kheiri, Ahmed, 53 Lecoutre, Christophe, 82, 123 Kim, Yong-Hyuk, 57, 94 Ledo, Luiz, 47 Kitamura, Tomofumi, 46 Lee, Sangyeop, 90, 138 Kittel, Fabian, 98 Legovi´c, Tarzan, 96 Klanke, Christian, 44 Lehman, Joel, 92 Klijn, Daan, 93 Lehre, Per, 141, 142 Knowles, Joshua, 91, 117 Lehre, Per Kristian, 38, 39, 90, 91 Kobayashi, Yuta, 95 Leitner, Sebastian, 68 Koenig, Reinhard, 94 Lemtenneche, Sami, 99 Kohira, Takehisa, 53 Leon, Coromoto, 49 Koll, Charles, 96 Lepagnot, Julien, 68 Komarnicki, Marcin, 45, 92, 121 Lepech, Michael, 52 Kong, Lingpeng, 55 Lezama, Fernando, 68 Kononova, Anna, 44, 54, 55 Li, Jian-Yuian, 98 Konotopska, Kateryna, 54 Li, Ke, 40, 49, 95 Kormushev, Petar, 80, 133 Li, Ling-Yu, 93 Korošec, Peter, 39, 67, 80, 84, 93, 123, 131 Li, Minyi, 84, 116 Koslowski, Christian, 44 Li, Xiaodong, 68, 84, 98, 116 Kostovska, Ana, 98 Li, Xinlu, 45 Kostrzewa, Daniel, 95 Li, Yiming, 93 Kovalchuk, Yevgeniya, 97 Liang, Jason, 86, 113 Kovalenko, Yulia, 82, 131 Liang, Yiwen, 99 Kovrizhnykh, Nikolai, 53 Liapis, Antonios, 89, 94, 108 Koza, Jan, 90, 118 Liefooghe, Arnaud, 45, 46, 50, 76, 83, 88, 111, 116 Kraneveld, Aletta, 85, 136 Lima, Beatriz, 48 Krauss, Oliver, 82, 130 Lin, Juan, 83, 104 Krauss, Rune, 94 Lira, Rodrigo, 83, 104 Krejca, Martin, 77, 81, 135 Lissovoi, Andrei, 81, 129 Kretowski, Marek, 85, 100, 136 Liu, Alex, 49 Krichmar, Jeffrey, 98 Liu, Dayiheng, 96 Kronberger, Gabriel, 95 Liu, Shuangrong, 97 Kulikovskikh, Ilona, 96 Liventsev, Vadim, 98 AUTHOR INDEX 151

Lodha, Sachin, 52 Mayer, Alexandre, 99 Lombardo, Gianfranco, 48 Mayer, Benjamin, 76, 83, 111 Lomurno, Eugenio, 55 McCall, John, 45, 76, 88, 98, 100, 116 Lopes, Cláudio, 94 Medvet, Eric, 52, 55, 76, 81, 106 Lopes, Rodolfo, 49 Mei, Yi, 81, 88, 110, 127 Lopez Rincon, Alejandro, 85, 136 Mejia-de-Dios, Jesus-Adolfo, 96 Lorandi, Michela, 84, 132 Mello Román, Julio C., 99 Lotito, Quintino, 92, 109 Mencía, Carlos, 100 Louis, Sushil, 56, 96 Mendiburu, Alexander, 45, 98 Lourenço, Nuno, 97, 98 Mendoza-Maldonado, Lucero, 85, 136 Lu, Qiang, 94 Menezes, Ronaldo, 83, 104 Lucini, Biagio, 57 Mengshoel, Ole, 44, 93, 98, 100 Luley, Leopold, 54 Menzel, Stefan, 56, 93 Luo, Jake, 94 Merelo, Juan, 50 Luong, Ngoc, 45 Mersmann, Olaf, 99 Lyu, Zimeng, 55 Merten, Marcel, 94 Meshoul, Souham, 99 Müller, Meinard, 90, 138 Mettler, Henrik, 96 Mézard, Marc, 34 Meyerson, Elliot, 88, 137 Ma, Chi, 88, 127 Meynen, Gerben, 100 Ma, Jun, 97 Mezura-Montes, Efrén, 55 Ma, Zeyuan, 91, 139 Mezura-Montes, Efren, 96 Macedo, João, 95 Michalak, Krzysztof, 82, 87, 111, 129 Macedo, Mariana, 83, 104 Miikkulainen, Risto, 38, 86, 88, 113, 114, 137 Machado, Penousal, 40, 88, 97, 98, 127 Milani, Alfredo, 45 MacRae, Cameron, 49 Minku, Leandro, 93 Madera-Quintana, Julio, 67 Miralavy, Iliya, 100 Mahapatra, Nihar, 88, 137 Miranda, Gara, 95 Mailer, Christopher, 92, 109 Mironov, Maxim, 53 Maire, Frederic, 92, 108 Mironovich, Vladimir, 49 Majumdar, Somdeb, 87, 107 Mishra, Sumit, 97, 100 Makrehchi, Masoud, 53 Mitchell, Melanie, 35, 57 Malan, Katherine, 39, 45, 46 Mitchener, Garrett, 97 Mandelbaum, Amit, 90, 138 Miyagi, Atsuhiro, 80, 123 Manson Brown, Stephanie, 88, 138 Mkaouer, Mohamed Wiem, 40 Manzoni, Luca, 86, 126 Molitor, Louise, 77, 81, 135 Marcelli, Angelo, 55 Montrone, Teresa, 98 Marchi, Mariapia, 98 Moon, Byung-Ro, 90, 138 Marescaux, Eugénie, 91, 117 Moon, Seung-Hyun, 57 Mariot, Luca, 54, 86, 126 Moore, Jason, 50, 57, 77, 83, 125 Marques, Lino, 95 Mordonini, Monica, 48 Marrero, Alejandro, 49 Moreno, Matthew, 48 Martí, Luis, 41 Moreno-Fernandez-de-Leceta, Aitor, 54 Martínez-López, Yoan, 67 Morinaga, Daiki, 91, 143 Martin, Lukas, 95 Moses, Jarrod, 45 Martinho, Lucas, 96 Mostaghim, Sanaz, 100 Martins, Flávio, 94 Motta, Mariele, 95 Martins, Tiago, 88, 127 Mouret, Jean-Baptiste, 39 Mata, Gabino, 56 Mousavi, Mohsen, 100 Mathias, H., 96 Mousavirad, Seyed Jalaleddin, 51 Matteucci, Matteo, 55 Mousin, Lucien, 49, 82, 123 152 AUTHOR INDEX

Mrkvicka, Christoph, 94 Oliveira, Marcos, 83, 104 Mueller, Andreas, 97 Oliveto, Pietro, 38, 39, 81, 129 Mujtaba, Dena, 88, 137 Olmscheid, Christian, 97 Mukhopadhyay, Anirban, 87, 112 Opoku, Daniel, 93 Munro, Paul, 98 Oregi, Izaskun, 54 Musliu, Nysret, 92, 94, 140 Orhand, Romain, 96 Myller, Michal, 100 Orlosky, Jason, 80, 134 Orzechowski, Patryk, 50, 57 Nadizar, Giorgia, 55 Osaba, Eneko, 54 Naharro, Pablo, 67 Ostermann, Fabian, 90, 138 Najarro, Elias, 68 Ou, Wen-Jie, 93 Nakamura, Yoshiki, 89, 114 Ouni, Ali, 40 Nakata, Masaya, 38, 89, 114 Ozlen, Melih, 49 Naldini, Federico, 94 Nalepa, Jakub, 95, 100 Pätzel, David, 52 Nan, Yang, 84, 115 Palar, Pramudita Satria, 53 Nanai, Kouki, 48 Palm, Rasmus Berg, 68 Naredo, Enrique, 44 Pandher, Samarpreet Singh, 52 Nasri, Sonia, 57, 100 Pang, Lie Meng, 84, 115 Naujoks, Boris, 53, 68 Pang, Wei, 47, 86, 113 Nayeem, Muhammad Ali, 53 Panholzer, Torsten, 69 Nazmi, Shabnam, 86, 113 Panov, Panˇce, 98 Neumann, Aneta, 39, 44, 77, 81, 85, 90, 92, 110, 120, Paolo, Giuseppe, 47, 48, 76, 81, 106 121, 139, 142 Papa, Gregor, 40 Neumann, Frank, 39, 44, 77, 81, 85, 90, 92, 98, 109, Pappa, Gisele, 47, 86, 135 110, 120, 121, 139, 142 Paquete, Luís, 38, 53, 57, 83, 111 Neumann, Gerhard, 48 Parker, Brandon, 90, 139 Nguyen, Bach, 100 Parque, Victor, 50, 51, 100 Nguyen, Thanh, 100 Parrend, Pierre, 96 Nichele, Stefano, 55 Paruchuri, Praveen, 93 Nickerson, Kyle, 99 Patton, Robert, 50, 95 Nikfarjam, Adel, 85, 121 Patwardhan, Shuchita, 55 Nikitin, Nikolay, 57 Paulissen, Spencer, 95 Nilsson, Olle, 77, 85, 134 Pełka, Przemysław, 95 Nitschke, Geoff, 81, 92, 94, 95, 106, 109 Peña, Jose, 67 Nitz, Douglas, 98 Pedersen, Joachim, 80, 133 Noguchi, Hayato, 80, 122 Pedram, Mahdi, 51 Nono Saha, Cyrille Merleau, 85, 136 Peeva, Dessislava, 99 Nuhu, Abdul-Rauf, 93 Pei, Wenbin, 89, 96, 114 Nunes, Matheus, 86, 135 Peine, Arne, 95 Nurcahyadi, Teddy, 49 Pellegrini, Paola, 94 Pellegrino, Felice Andrea, 55 O’Neill, Michael, 99 Peng, Yiming, 84, 115 O’Reilly, Una-May, 38, 40, 56, 76, 82, 85, 100, 112, 123 Pereira, Jordi, 67 Oara, Daniel, 50 Perez Romero, Carmina, 85, 136 Obst, Oliver, 93 Perez-Cisnero, Marco, 47 Ochoa, Gabriela, 39, 45, 46, 81, 82, 87, 110, 112, 120 Petke, Justyna, 45 Ofria, Charles, 48 Petkovi´c, Milan, 98 Okulewicz, Michał, 46 Petrosino, Giuseppe, 48 Olague, Gustavo, 47 Petrovici, Mihai, 96 Oliva, Diego, 47, 51 Pezzè, Mauro, 84, 132 AUTHOR INDEX 153

Pfisterer, Florian, 97 Ramamoorthy, Muhilan, 88, 137 Picard, Cyril, 80, 128 Ramamurthy, Arun, 52 Picek, Stjepan, 40, 54, 86, 92, 122, 126 Ramos-Figueroa, Octavio, 93 Piechaczek, Szymon, 95 Randone, Francesca, 52 Piga, Dario, 51 Ranzato, Francesco, 85, 113 Pigozzi, Federico, 76, 81, 106 Rapin, Jérémy, 68 Pikalov, Maxim, 49 Ratcliffe, Simon, 44 Pinheiro, Diego, 83, 104 Raw, Leanne, 92, 109 Pinto Roa, Diego P.,99 Ray, Tapabrata, 90, 94, 139 Pitra, Zbynˇek,90, 118 Raymond, Christian, 98 Pitt, Jeremy, 80, 133 Rebolledo Coy, Margarita, 68, 97 Planini´c,Lucija, 54 Reese, Lennard, 99 Plaquevent-Jourdain, Baptiste, 46 Regis, Rommel, 52 Platzner, Marco, 52 Regnier-Coudert, Olivier, 45 Pluhacek, Michal, 51, 67 Rehbach, Frederik, 68, 99 Poggi, Agostino, 48 Rehbach, Nicolas, 99 Polonskaia, Iana, 57 Reid, Kenneth, 100 Ponti, Andrea, 99 Reid, William, 44 Pope, Aaron, 83, 124 Ren, Yu, 100 Popovski, Gorjan, 80, 92, 123, 125 Renc, Paweł, 50, 57 Porter, Barry, 98 Reynolds, Mark, 92, 121 Posik, Petr, 46 Rezazadeh Kalehbasti, Pouya, 52 Povéda, Guillaume, 45 Ribeiro, Denys, 96 Pozo, Aurora, 47 Richoux, Florian, 95 Prakash, Ved, 97, 100 Riege, Jon, 100 Prasetyo, Judhi, 84, 105 Risi, Sebastian, 48, 68, 80, 96, 133 Preuss, Mike, 68, 92 Ritt, Marcus, 87, 112 Probst, Malte, 81, 129 Robertson, Jake, 94 Probst, Philipp, 97 Robilliard, Denis, 97 Przewozniczek, Michal, 45, 82, 92, 121, 131 Rochelli, Marco, 76, 81, 106 Puente, Cesar, 47 Rodríguez-González, Ansel Y., 67 Purshouse, Robin, 50 Rodrigues, Rodrigo, 96 Rodriguez Papa, Santiago, 48 Qian, Chao, 39 Rodriguez-Esparza, Erick, 47 Qian, Xiang, 57 Rodrigurez, Joaquin, 94 Qin, Xiaoyu, 90, 141 Rogalski, Marek, 95 Qing, Yang, 88, 127 Rokhsatyazdi, Ehsan, 49 Qiu, Feiyue, 100 Romero, Ruben, 68 Qiu, Hao, 53 Ronghui, Luo, 53 Qiu, Qicang, 100 Rosenberg, Manou, 92, 121 Qiu, Xin, 88, 137 Rothlauf, Franz, 38, 77, 80, 81, 83, 97, 100, 126, 128, Quemy, Alexandre, 50 129 Quevedo, Jose, 47, 99 Rouzaud-Cornabas, Jonathan, 50 Quiroz-Castellanos, Marcela, 93 Roy, Nicolas, 99 Ruberto, Stefano, 77, 83, 125 Rachelson, Emmanuel, 86, 134 Rubino, Gerardo, 96 Radešˇcek,Janez, 67 Ruppert, Jean, 57 Rahat, Alma, 57, 82, 84, 130, 132 Ryan, Conor, 44 Rahnamayan, Shahryar, 49, 53 Ryczko, Wojciech, 100 Rajabi, Amirhossein, 90, 142 Rakicevic, Nemanja, 80, 133 Sánchez-Pi, Nayat, 41 154 AUTHOR INDEX

Saadatmand, Mehrdad, 51 Shin, Seung-Soo, 57 Sachdeva, Enna, 87, 107 Shir, Ofer, 38, 40, 44, 94 Sadowski, Krzysztof, 99 Shiraishi, Hiroki, 97 Saini, Anil, 96 Shlapentokh-Rothman, Michal, 82, 123 Sainz-Pardo Auñón, José Luis, 67 Shnytkin, Matvey, 49 Sakal, James, 97 Siegler, Jason, 93 Sakuma, Jun, 77, 80, 81, 91, 123, 135, 143 Sielski, Piotr, 95 Sakurai, Tetsuya, 95 Sierra, María R., 100 Salehi, Achkan, 89, 108 Silva, Rodrigo, 49 Saletta, Martina, 56 Silva-Muñoz, Moisés, 44 Salomon, Shaul, 50 Singh, Hemant, 90, 94, 139 Sambo, Aliyu Sani, 97 Sinha, Nilotpal, 86, 134 Samele, Stefano, 55 Sinha, Ujjayant, 88, 138 Sanchez-Anguix, Víctor, 44 Skjærseth, Eirik, 98 Santana, Roberto, 98 Smerlak, Matteo, 85, 136 Santucci, Valentino, 45 Smith, Robert, 68, 87, 106 Sarker, Ruhul, 53 Soares, Joao, 68 Sarro, Federica, 100 Sobania, Dominik, 77, 83, 97, 100, 126 Sato, Hiroyuki, 97 Sodhi, Manbir, 47, 99 Saurabh, Saket, 52 Song, An, 93 Scafuri, Umberto, 55 Sonntag, Christian, 44 Schaefer, Gerald, 51 Soubervielle-Montalvo, Carlos, 47 Schiattarella, Roberto, 99 Spanellis, Christina, 81, 106 Schiffmann, Jürg, 80, 128 Spector, Lee, 41, 55, 96 Schiller, Leon, 77, 81, 135 Spettel, Patrick, 77, 87, 118 Schmeink, Anke, 95 Srinivasan, Dipti, 91, 117 Schmidt, Maximilian, 96 Sriwastava, Ambuj, 50 Schoenauer, Marc, 50, 86, 126 Stöger, Markus, 44 Schrum, Jacob, 87, 97, 107 Stützle, Thomas, 39, 44 Schuman, Catherine, 50 Stadem, David, 55 Schweikl, Sebastian, 89, 140 Stanley, Kenneth, 92 Schweim, Dirk, 80, 100, 128 Steckel, Kirby, 97 Scoczynski, Marcella, 47, 68 Stegherr, Helena, 54 Scott, Eric, 50, 55, 98 Stein, Anthony, 38, 94 Seals, Deacon, 83, 124 Steinhaus, Meghan, 99 Segredo, Eduardo, 49, 95 Stellaccio, Luca, 55 Semenchikov, Dmitriy, 53 Sterzentsenko, Vladimiros, 67, 85, 136 Sen, Amrita, 99 Stewart, Brooke, 81, 106 Sendhoff, Bernhard, 54, 56, 93 Stockton, Phil, 100 Senn, Walter, 96 Stolfi, Daniel, 98 Sfikas, Konstantinos, 89, 108 Stone, Christopher, 87, 107 Shah, Hanifa, 97 Stork, Jörg, 55 Shahrzad, Hormoz, 86, 113 Su, Junpeng, 67 Shang, Ke, 76, 84, 88, 115, 116 Subbotin, Aleksandr, 99 Shang, Lin, 89, 93, 114 Sudholt, Dirk, 78, 87, 141 Shankar, Anirudh, 86, 134 Suganthan, Ponnuthurai, 68 Sharma, Naveen, 57 Sullivan, Joe, 44 Shavarani, Seyed, 91, 117 Sun, Jianyong, 95 Shechtman, Natali, 90, 138 Sun, Jiaze, 97 Shen, Ailing, 83, 104 Sun, Yanan, 96 Shimizu, Hiroki, 94 Sutton, Andrew, 90, 142 AUTHOR INDEX 155

Syrotiuk, Violet, 88, 137 Tusar, Tea, 46 Szczepanski, Nicolas, 82, 123 Ullah, Sibghat, 56 Tadokoro, Masakazu, 97 Ungredda, Juan, 98 Takadama, Keiki, 97 Urbanowicz, Ryan, 56 Takahashi, Daniel, 99 Urquhart, Neil, 46, 98 Tan, Chengyu, 99 Tanabe, Ryoji, 91, 119 Vázquez Noguera, José Luis, 99 Tanabe, Takumi, 77, 81, 135 Vale, Zita, 68 Tang, Zhenzhou, 97 Valledor Pellicer, Pablo, 44 Tarantino, Ernesto, 55 van Driessel, Tobias, 54 Tarapore, Danesh, 98 van Rijn, Jan, 97 Tari, Sara, 87, 112 van Stein, Bas, 55 Tauritz, Daniel, 38, 47, 56, 83, 95, 124 van Wachem, Berend, 100 Taw, Lydia, 83, 104 Vanden Berghe, Greet, 44 Taylor, Kendall, 84, 116 Vanschoren, Joaquin, 97 Teixeira, Otávio, 96 Varadarajan, Swetha, 82, 120 Templier, Paul, 86, 134 Varela, Ramiro, 100 Tenenbaum, Joshua, 34 Varga, Marius, 46 Terragni, Valerio, 77, 83, 84, 125, 132 Vargas-Hákim, Gustavo-Adolfo, 55 Teytaud, Olivier, 68 Vasile, Massimiliano, 53 Thawonmas, Ruck, 80, 122 Vatolkin, Igor, 90, 138 Thierens, Dirk, 38, 45, 54, 93, 99 Veerapen, Nadarajen, 45, 46, 49, 82, 123 Thite, Anish, 49 Ventresque, Anthony, 80, 128 Thoelke, Seth, 55 Verel, Sébastien, 45, 46, 50, 76, 88, 116 Tian, Ye, 84, 132 Vermetten, Diederick, 47, 55, 98 Tichelmann, Patrick, 55 Verwer, Sicco, 51 Tinós, Renato, 82, 87, 112, 120 Vié, Aymeric, 49 Tisdale, Braden, 83, 124 Vighi, Alexis, 86, 126 Togelius, Julian, 91, 119 Viktorin, Adam, 51, 67 Tomaiuolo, Michele, 48 Villar-Rodriguez, Esther, 54 Tomassini, Marco, 81, 110 Vincalek, Jakub, 97 Tomczyk, Michał, 91, 117 Vinje, Harald, 98 Tonda, Alberto, 85, 136 Virgolin, Marco, 52, 88, 127 Tonella, Paolo, 84, 132 Vitiello, Autilia, 99 Tong, Hao, 93 Vogl, Sebastian, 89, 140 Tornede, Alexander, 89, 115 Volf, Dmitriy, 53 Tornede, Tanja, 89, 114 Volz, Vanessa, 53, 68 Torsney-Weir, Thomas, 57 Von Zuben, Fernando, 82, 119 Toutouh, Jamal, 40, 76, 85, 112 Toyoda, Masashi, 94 Wójcik, Krzysztof, 83, 105 Trojanowski, Krzysztof, 83, 105 W ˛as, Jarosław, 50, 57 Tušar, Tea, 39, 68 Wagner, Alexander, 52, 94 Tuci, Elio, 84, 105 Wagner, Markus, 39, 45, 68, 98 Tukker, Teus, 44 Wagner, Stefan, 46, 50, 68 Tulczyjew, Lukasz, 100 Wahab, Magd, 100 Tumer, Kagan, 87, 94, 96, 107 Wahde, Mattias, 52 Tumpach, Jiˇrí,90, 118 Walker, David, 46 Tunalı, Okan, 44 Walker, Kathryn, 96 Tupsamudre, Harshal, 52 Wallace, Mark, 98 Turpin, Laurent, 50 Walton, Sean, 97 156 AUTHOR INDEX

Wang, Bin, 93, 96 Yannakakis, Georgios, 89, 94, 108 Wang, Bing, 94 Yao, Xin, 80, 92, 93, 125, 128 Wang, Dongmei, 99 Yates, Connor, 94 Wang, Hao, 47, 54–56, 90, 118 Yazmir, Boris, 94 Wang, Hua, 98 Ye, Furong, 97 Wang, Lin, 97 Ye, Michael, 88, 138 Wang, Liping, 100 Yin, Kaiou, 89, 140 Wang, Shaolin, 81, 110 Yu, Tong, 100 Wang, Wenjun, 47, 86, 113 Yuan, Yingfang, 47, 86, 113 Wang, Xiaohao, 57 Yuanming, Zhu, 53 Wang, Yuhang, 84, 132 Yue, Tao, 82, 89, 130, 140 Wang, Zhiguang, 94 Wanner, Elizabeth, 94 Z˘avoianu, Alexandru-Ciprian, 76, 88, 98, 116 Wauters, Tony, 44 Zaborski, Mateusz, 46 Wei, Huang, 53 Zaefferer, Martin, 55, 99 Weidmann, Nils, 89, 140 Zaharie, Daniela, 55 Weigend, Fabian, 93 Zakir, Raina, 84, 105 Weintritt, Wolfgang, 92, 140 Zaman Farsa, Davood, 49 Weir, Terence, 53 Zambrano, Davide, 92, 109 Weise, Thomas, 45, 53 Zanella, Marco, 85, 113 Werth, Bernhard, 46, 68 Zapotecas-Martínez, Saúl, 77, 88, 116 Wever, Marcel, 89, 115 Zappetti, Davide, 92, 109 While, Lyndon, 92, 121 Zardini, Enrico, 92, 109 Whitley, Darrell, 38, 82, 87, 112, 120 Zarpalas, Dimitrios, 67, 85, 136 Wild, Alexander, 98 Zeeuwe, Daan, 97 Wilson, Allan, 100 Zeng, Shutong, 44 Wilson, Dennis, 86, 134 Zhan, Zhi-Hui, 98 Winkler, Stephan, 44, 95 Zhang, Jinyuan, 84, 115 Winter, Felix, 92, 94, 140 Zhang, Jun, 98 Witkowski, Olaf, 84, 105 Zhang, Juntao, 94 Witt, Carsten, 81, 90, 129, 142 Zhang, Lin, 100 Wittenberg, David, 80, 97, 128 Zhang, Longfei, 95 Wood, Michael, 84, 132 Zhang, Mengjie, 40, 81, 88, 89, 93, 96, 98, 100, 110, 114, Woodward, John, 38, 39, 47, 97 127 Worring, Adrian, 76, 83, 111 Zhang, Qiaozhi, 92, 125 Wright, Alden, 86, 126 Zhang, Qingfu, 40 Wu, Annie, 96 Zhang, Xiao, 88, 127 Wu, Zhize, 45 Zhang, Xingyi, 84, 132 Zhang, Yiqing, 95 Xia, Haowen, 88, 127 Zhao, Shuai, 82, 131 Xiao, Jianhua, 84, 132 Zhao, Ying, 56 Xie, Yue, 90, 92, 139, 142 Zheng, Wei, 95 Xu, Congwen, 94 Zheng, Weijie, 80, 84, 92, 125, 128, 131 Xue, Bing, 40, 89, 93, 96, 98, 100, 114 Zhong, Yiwen, 83, 104 Zhou, Yu, 88, 127 Yan, Xuyang, 93 Zhou, Yufan, 83, 104 Yan, Yiming, 50 Zhu, Shibei, 48 Yan, Yiyang, 88, 138 Zielniewicz, Piotr, 84, 115 Yang, Jingyuan, 88, 138 Zille, Heiner, 100 Yang, Xinhua, 83, 104 Zincir-Heywood, Nur, 56 Yang, Xinmin, 99 Zioulis, Nikolaos, 67, 85, 136 AUTHOR INDEX 157

Zou, Xinyun, 98 Zutty, Jason, 49 Zullich, Marco, 55