Conference Program
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Evolutionary Data Mining Approach to Creating Digital Logic
EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC James F. Smith III, ThanhVu H. Nguyen Code 5741, Naval Research Laboratory, Washington, DC, 20375-5320, USA Keywords: Optimization problems in signal processing, signal reconstruction, system identification, time series and system modeling. Abstract: A data mining based procedure for automated reverse engineering has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts’ rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Significant mathematical formalism and experimental results related to GP based data mining for reverse engineering will be provided. 1 INTRODUCTION The rules are used to create a fitness function for the genetic program. Genetic program based data An engineer must design a signal that will yield a mining is then conducted (Bigus 1996, Smith 2003a, particular type of output from a sensor device (SD). -
Green Parallel Metaheuristics: Design, Implementation, and Evaluation
DOCTORAL THESIS Green Parallel Metaheuristics: Design, Implementation, and Evaluation PhD Thesis Dissertation in Computer Science Author Amr Abdelhafez Directors Enrique Alba Torres and Gabriel Luque Polo E. T. S. I. Informática Lenguajes y Ciencias de la Computación UNIVERSIDAD DE MÁLAGA (España) April 2020 AUTOR: Amr Abdelhafez Abdelsamee Galal http://orcid.org/0000-0003-0469-3791 EDITA: Publicaciones y Divulgación Científica. Universidad de Málaga Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial- SinObraDerivada 4.0 Internacional: http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode Cualquier parte de esta obra se puede reproducir sin autorización pero con el reconocimiento y atribución de los autores. No se puede hacer uso comercial de la obra y no se puede alterar, transformar o hacer obras derivadas. Esta Tesis Doctoral está depositada en el Repositorio Institucional de la Universidad de Málaga (RIUMA): riuma.uma.es DECLARACIÓN DE AUTORÍA Y ORIGINALIDAD DE LA TESIS DOCTORAL Considerando que la presentación de un trabajo hecho por otra persona o la copia de textos, fotos y gráficas sin citar su procedencia se considera plagio, el abajo firmante Don Amr Abdelhafez con NIE: X que presenta la tesis doctoral con el título: Metaheurísticas Paralelas Verdes: Diseño, Implementación y Evaluación, declara la autoría y asume la originalidad de este trabajo, donde se han utilizado distintas fuentes que han sido todas citadas debidamente en la memoria. Y para que así conste firmo el presente documento en Málaga a……………….. El autor: ………………….….. Departamento de Lenguajes Y Ciencias de La Computación Escuela Técnica Superior de Ingeniería Informática Universidad de Málaga El Dr. -
New Approaches in Parallel Algorithm Portfolios for Metaheuristic Optimization
New Approaches in Parallel Algorithm Portfolios for Metaheuristic Optimization A Dissertation submitted to the designated by the General Assembly of Special Composition of the Department of Computer Science and Engineering Examination Committee by Dimitrios Souravlias in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY University of Ioannina June 2017 Advisory Committee • Konstantinos E. Parsopoulos, Associate Professor, Department of Computer Science & Engineering, University of Ioannina, Greece • Enrique Alba, Professor, Department of Computer Science & Programming Languages, University of Málaga, Spain • Konstantina Skouri, Associate Professor, Department of Mathematics, Univer- sity of Ioannina, Greece Examination Committee • Konstantinos E. Parsopoulos, Associate Professor, Department of Computer Science & Engineering, University of Ioannina, Greece • Enrique Alba, Professor, Department of Computer Science & Programming Languages, University of Málaga, Spain • Konstantina Skouri, Associate Professor, Department of Mathematics, Univer- sity of Ioannina, Greece • Isaac Lagaris, Professor, Department of Computer Science & Engineering, Uni- versity of Ioannina, Greece • Ilias Kotsireas, Professor, Department of Physics & Computer Science, Wilfrid Laurier University, Canada • Vassilios Dimakopoulos, Associate Professor, Department of Computer Science & Engineering, University of Ioannina, Greece • Dimitrios Papageorgiou, Associate Professor, Department of Materials Science & Engineering, University of Ioannina, Greece Dedication To my family Acknowledgements First and foremost I would like to express my deepest gratitude to my advisor, Professor Konstantinos E. Parsopoulos, for his valuable guidance and continuous support from the beginning of my PhD studies. Our excellent collaboration has given me the opportunity to acquire important knowledge and skills that have helped me in my research endeavor. Our long discussions and his constant encouragement have greatly contributed to my scientific and personal development. -
A Genetic Algorithm Solution Approach Introd
A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach Majeed HEYDARI University of Zanjan, Zanjan, Iran [email protected] Amir YOUSEFLI Imam Khomeini International University, Qazvin, Iran [email protected] Abstract. Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable. Keywords: market basket analysis, association rule, non-linear programming, genetic algorithm, optimization. Please cite the article as follows: Heydary, M. and Yousefli, A. (2017), “A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach”, Management & Marketing. Challenges for the Knowledge Society, Vol. 12, No. 1, pp. 1- 11. DOI: 10.1515/mmcks-2017-0001 Introduction Nowadays, data mining is widely used in several aspects of science such as manufacturing, marketing, CRM, retail trade etc. Data mining or knowledge discovery is a process for data analyzing to extract information from large databases. -
Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring
Journal of Civil Engineering and Construction 2020;9(1):14-23 https://doi.org/10.32732/jcec.2020.9.1.14 Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring Meisam Gordan, Zubaidah Binti Ismail, Hashim Abdul Razak, Khaled Ghaedi, Haider Hamad Ghayeb Department of Civil Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia E-mail: [email protected] Received: 27 July 2019; Accepted: 24 August 2019; Available online: 20 November 2019 Abstract: In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms. Keywords: Structural damage detection; Data mining; Metaheuristic algorithms; Optimization. 1. Introduction In-service aerospace, mechanical, and civil structures are damage-prone during their service life. -
Multi Objective Optimization of Classification Rules Using Cultural Algorithms
Available online at www.sciencedirect.com Procedia Engineering Procedia Engineering 00 (2011) 000–000 Procedia Engineering 30 (2012) 457 – 465 www.elsevier.com/locate/procedia International Conference on Communication Technology and System Design 2011 Multi Objective Optimization of classification rules using Cultural Algorithms Sujatha Srinivasana, Sivakumar Ramakrishnana, a* aDept. of Computer Science, AVVM Sri Pushpam College, Poondi, Tanjore-613503, Tamil Nadu, India Abstract Classification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS’s) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011 Keywords: Multi-objective-optimization; Classification; Cultural Algorithm; Data mining. 1. Introduction Classification rule mining is a class of problems which is the most sought out by decision makers since they produce comprehensible form of knowledge. The rules produced by the rule mining approach are evaluated using various metrics which are called the properties of the rule. -
Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction
International Journal of Machine Learning and Computing, Vol. 2, No. 3, June 2012 Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction Jenn-Long Liu, Chien-Liang Chen, and Hsing-Hui Yang instances of insurance cases for data mining. The 5000 Abstract—This study proposes two kinds of Evolutionary instances are divided into 4000 instances to be the training set Data Mining (EvoDM) algorithms to the insurance fraud and 1000 instances to be the test set. Furthermore, this work prediction. One is GA-Kmeans by combining K-means applies K-means, GA-Kmeans and MPSO-Kmeans algorithm with genetic algorithm (GA). The other is algorithms to evaluate the fraud or not from the training set MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO). The and also evaluate the accuracy of fraud prediction for the test dataset used in this study is composed of 6 attributes with 5000 set. instances for car insurance claim. These 5000 instances are divided into 4000 training data and 1000 test data. Two II. CRISP-DM different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training CRISP-DM (Cross Industry Standard Process for Data set and (b) averaging all data of training set, respectively. Mining) is a data mining process model that describes Thereafter, the proposed GA-Kmeans and MPSO-Kmeans are commonly used approaches for expert data miners use to employed to determine the optimal weights and final cluster solve problems. CRISP-DM was conceived in late 1996 by centers for attributes, and the accuracy of prediction for test set SPSS (then ISL), NCR and DaimlerChrysler (then is computed based on the optimal weights and final cluster Daimler-Benz). -
Evolutionary Data Mining for Link Analysis: Preliminary Experiments on a Social Network Test Bed
Evolutionary Data Mining for Link Analysis: Preliminary Experiments on a Social Network Test Bed William H. Hsu Andrew L. King Martin S. R. Paradesi Tejaswi Pydimarri Tim Weninger Department of Computing and Information Sciences, Kansas State University 234 Nichols Hall, Manhattan, KS 66506 (785) 532-6350 {bhsu | aking | pmsr | tejaswi | weninger}@ksu.edu ABSTRACT between communities and users they denote accepted members In this paper, we address the problem of graph feature extraction and moderator privileges. Specific recommendation targets for and selection for link analysis in weblogs and similar social weblogs include links (new friends and subscriberships), security networks. First, we present an approach based on collaborative levels (link strength), requests for reciprocal links (trust, recommendation using the link structure of a social network and membership and moderatorship applications), and definition of content-based recommendation using mutual declared interests. security levels (filters). There are analogous applicatons of this Next, we describe the application of this approach to a small functionality in social networks such as citation and collaboration representative subset of a large real-world social network: the networks. user/community network of the blog service LiveJournal. We We investigate the problem of link recommendation in such then discuss the ground features available in LiveJournal’s public weblog-based social networks and describe an annotated graph- user information pages and describe some graph algorithms for based representation for such networks. Our approach uses graph analysis of the social network along with a feature set for feature analysis to recommend links (u, v) given structural classifying users as friends or non-friends. -
Deep Neuroevolution: Smart City Applications
UNIVERSIDAD DE MÁLAGA DOCTORAL THESIS Deep Neuroevolution: Smart City Applications Author: Supervisor: M.Sc. Andrés CAMERO Prof. Dr. Enrique ALBA A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in the Programa de Doctorado en Tecnologías Informáticas Departmento de Lenguajes y Ciencias de la Compuación E.T.S.I. Informática February 12, 2021 ! AUTOR: Andrés Camero Unzueta http://orcid.org/0000-0002-8152-9381 EDITA: Publicaciones y Divulgación Científica. Universidad de Málaga ! Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial- SinObraDerivada 4.0 Internacional: http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode Cualquier parte de esta obra se puede reproducir sin autorización pero con el reconocimiento y atribución de los autores. No se puede hacer uso comercial de la obra y no se puede alterar, transformar o hacer obras derivadas. Esta Tesis Doctoral está depositada en el Repositorio Institucional de la Universidad de Málaga (RIUMA): riuma.uma.es iii M.Sc. Andrés Camero Unzueta, estudiante del programa de doctorado Tec- nologías Informáticas de la Universidad de Málaga, autor de la tesis, presentada para la obtención del título de doctor por la Universidad de Málaga, titulada: Deep Neuroevolution: Smart City Applications Realizada bajo la tutorización y dirección del Prof. Dr. Enrique Alba Torres. Declaro que La tesis presentada es una obra original que no infringe los derechos de propiedad intelectual ni los derechos de propiedad industrial u otros, conforme al ordenamiento jurídico vigente (Real Decreto Legislativo 1/1996, de 12 de abril, por el que se aprueba el texto refundido de la Ley de Propiedad Intelectual, regularizando, aclarando y armonizando las disposiciones legales vigentes sobre la materia), modificado por la Ley 2/2019, de 1 de marzo. -
Investigating Recurrent Neural Network Memory Structures Using Neuro-Evolution
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution Travis Desell ([email protected]) Associate Professor Department of Software Engineering Co-Authors: AbdElRahman ElSaid (PhD GRA) Alex Ororbia (Assistant Professor, RIT Computer Science) Collaborators: Steven Benson, Shuchita Patwardhan, David Stadem (Microbeam Technologies, Inc.) James Higgins, Mark Dusenbury, Brandon Wild (University of North Dakota) Overview • What is Neuro-Evolution? • Distributed Neuro- Evolution • Background: • Recurrent Neural • Results Networks for Time Series Prediction • Future Work • Recurrent Memory Cells • Discussion • EXAMM: • NEAT Innovations • Edge and Node Mutations • Crossover The 2019 Genetic and Evolutionary Computation Conference Prague, Czech Republic July 13th-17th, 2019 Motivation The 2019 Genetic and Evolutionary Computation Conference Prague, Czech Republic July 13th-17th, 2019 Neuro-Evolution • Applying evolutionary strategies to artificial neural networks (ANNs): • EAs to train ANNs (weight selection) • EAs to design ANNs (what architecture is best?) • Hyperparameter optimization (what parameters do we use for our backpropagation algorithm) The 2019 Genetic and Evolutionary Computation Conference Prague, Czech Republic July 13th-17th, 2019 Neuro-Evolution for Recurrent Neural Networks • Most people use human-designed ANNs, selecting from a few architectures that have done well in the literature. • No guarantees these are most optimal (e.g., in size, predictive ability, generalizability, robustness, etc). • Recurrent -
Systolic Genetic Search, a Parallel Metaheuristic for Gpus
Tesis de Doctorado en Informática Systolic Genetic Search, A Parallel Metaheuristic for GPUs Martín Pedemonte Director de Tesis: Prof. Enrique Alba Torres Co-Director de Tesis: Prof. Francisco Luna Valero Director Académico: Prof. Héctor Cancela PEDECIBA Informática Instituto de Computación – Facultad de Ingeniería Universidad de la República Setiembre 2017 “Life is what happens to you, while you’re busy making other plans” John Lennon, Beautiful Boy (Darling Boy) To Juan Pablo and Isabel, two by-products of this thesis. Agradecimientos Finalmente ha llegado el día y el Doctorado está terminando. Este momento nunca hubiera sido posible sin la colaboración de un conjunto de personas, que me apoyaron a lo largo de este proceso, a las que quiero expresar mi agradecimiento. En primer lugar, quiero agradecer a mis directores de Tesis Enrique y Paco por su guía académica, su aporte a este trabajo, y la confianza en mí y en mi trabajo. También agradezco a Héctor, quien fue mi director académico, por su ayuda y su estímulo. A Pablo y Ernesto por aportarme otras perspectivas, por los intercambios de ideas y por sus valiosos comentarios que contribuyeron con este trabajo. A Eduardo por sus sugerencias e ideas para el diseño de una nueva grilla. Agradezco a mi compañera Leonella por su apoyo incondicional y su paciencia infinita, y también por haberme facilitado las cosas para que pudiera dedicarle tiempo a esta tesis. También agradezco a mis hijos Juan Pablo e Isabel por todos los ratos que les tuve que robar. A mi madre Graciela y a mi hermano Gerardo por su continuo aliento y por su cariño. -
CURRICULUM VITAE Carlos Artemio Coello Coello February 27Th, 2021
CURRICULUM VITAE Carlos Artemio Coello Coello February 27th, 2021 Personal Information DATE OF BIRTH : October 18, 1967 PLACE OF BIRTH : Tonala,´ Chiapas, Mexico´ PHONE : +52 (55) 5747 3800 x 6564 EMAIL : [email protected] WORLD WIDE WEB : http://delta.cs.cinvestav.mx/˜ccoello CURRENT WORKPLACE: Departamento de Computacion´ CINVESTAV-IPN, Av. IPN No. 2508, Mexico,´ D.F. 07360, Mexico´ CURRENT POSITION: Investigador CINVESTAV 3F (Professor with Distinction) ORCID ID: 0000-0002-8435-680X ResearcherID: B-1845-2008 Scopus Author ID: 7003514400 Academic Degrees • BSc in Civil Engineering, Universidad Autonoma´ de Chiapas, 1985–1990. Degree awarded in 1991 with the thesis “Analysis of Grid Structures using a personal computer (stiffness method)” (summa cum laude). • Master of Science in Computer Science, Tulane University, New Orleans, Louisiana, EUA, 1991–1993. Degree awarded in December, 1993. • PhD in Computer Science, Tulane University, New Orleans, Louisiana, EUA, 1993–1996. Degree awarded in May, 1996, with the thesis entitled “An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design”. Advisor: Dr. Alan D. Christiansen. Most Relevant Publications Monographic Books 1. Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers, New York, USA, May 2002, ISBN 0-3064-6762-3. 2. Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, New York, USA, Second Edition, ISBN 978-0-387-33254-3, September 2007. Edited Books 1. Carlos A. Coello Coello and Gary B. Lamont (eds), Applications of Multi-Objective Evolutionary Algorithms, World Scientific, Singapore, 2004, ISBN 981-256-106-4.