DOCSLIB.ORG

Evolving Neural Networks Using Behavioural Genetic Principles

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evolving Neural Networks Using Behavioural Genetic Principles Evolving Neural Networks Using Behavioural Genetic Principles Maitrei Kohli A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy Department of Computer Science & Information Systems Birkbeck, University of London United Kingdom March 2017 0 Declaration This thesis is the result of my own work, except where explicitly acknowledged in the text. Maitrei Kohli …………………. 1 ABSTRACT Neuroevolution is a nature-inspired approach for creating artificial intelligence. Its main objective is to evolve artificial neural networks (ANNs) that are capable of exhibiting intelligent behaviours. It is a widely researched field with numerous successful methods and applications. However, despite its success, there are still open research questions and notable limitations. These include the challenge of scaling neuroevolution to evolve cognitive behaviours, evolving ANNs capable of adapting online and learning from previously acquired knowledge, as well as understanding and synthesising the evolutionary pressures that lead to high-level intelligence. This thesis presents a new perspective on the evolution of ANNs that exhibit intelligent behaviours. The novel neuroevolutionary approach presented in this thesis is based on the principles of behavioural genetics (BG). It evolves ANNs’ ‘general ability to learn’, combining evolution and ontogenetic adaptation within a single framework. The ‘general ability to learn’ was modelled by the interaction of artificial genes, encoding the intrinsic properties of the ANNs, and the environment, captured by a combination of filtered training datasets and stochastic initialisation weights of the ANNs. Genes shape and constrain learning whereas the environment provides the learning bias; together, they provide the ability of the ANN to acquire a particular task. Ontogenetic adaptation was implemented via a local gradient-based search method, while phylogenetic evolution was implemented via a Darwinian, fitness-based selection approach. The project was structured as follows. Chapter 2 presents the novel neuro-evolutionary approach for evolving populations of neural networks, inspired from BG principles. In chapter 3, the framework is applied to an exemplar problem domain drawn from psychology, that of English past-tense acquisition within the field of child language development. This domain is notable because it is a quasi-regular or dual-natured task. Chapter 3 also introduces the analytical technique of assessing the ‘heritability’ of performance in a population of ANNs. Populations are created comprising identical and non-identical ‘twins’, so specified by the similarity of their artificial genomes. Heritability provides a scalable summary statistic of the net effect of all internal parameters on learning. However, it can only be quantified when variable also exists in the quality of the environment. The findings from the experimental evaluations demonstrated the effectiveness of the model and provided a basis to extend it to capture population-level differences within developmental settings. In the second half of the project, the framework was extended to model transfer learning, with a special focus on heterogeneous tasks. This simulated the neuroevolutionary scenario wherein population members can be required to learn tasks different from those for which they were selected. Chapter 4 lays out the theoretical issues in this field. Large-scale simulations, involving over 200,000 networks, then identified and tested two key factors that modulated the performance of the transfer model – the type of selection operator (chapter 5) and the nature of source task (chapter 6). By transferring the ‘general ability to learn’, the transfer model enabled a population of ANNs to acquire successfully five different heterogeneous tasks. Analyses of heritability and environmentability were utilised to reveal which factors were most responsible for variation in performance, and its improvement across generations. Crucially, these large-scale experiments demonstrated that it is possible for a population of ANNs to acquire multiple heterogeneous tasks, and that the heritability metric can be utilised to identify when negative transfer effects may occur. As discussed in chapter 7, the BG-inspired method therefore presents concrete progress in optimising those neurocomputational properties of ANNs relevant to enhance learning across multiple problem domains. 2 Acknowledgements I am truly grateful to all the people who supported me through this incredible journey. My sincerest thanks to My parents, you are my biggest strength and inspiration. I am so grateful for your unconditional love, prayers, and unwavering belief in me. Most of all, I am extremely grateful for everything you do for me. I dedicate this work to you. My brother, Jivak, for your constant support, encouragement, funny banter and for always being there for me. Thanks for being the best brother ever. My grandma, for loving me so much and for endless blessings. My best friend, Areej, through good times and bad you have been there for me always. Thank you for everything. Mrs. La Young Jackson, for guiding and helping me through visa processes numerous times. Ms. Kristina Freris, for advising and encouraging me when things got tough and overwhelming. I am really thankful. Oriental bank of commerce, for giving me education loan and thereby making finances easier for me. Dept. of Computer Science, Birkbeck, for awarding me with 3-year BEI studentship. Systems Group, for helping me numerous times with Matlab and Condor. Members of Developmental Neuro-cognition Lab, for giving me several opportunities to present my work and for all your constructive feedback and suggestions. Thanks to Prof. Michael Thomas for making me a member of DNL. My co-supervisor, Prof. Michael Thomas, for your guidance, encouragement, valuable feedback, hard-work and patience with me. You have taught me to always strive for perfection and to keep the goals and standards high. Working with you has been a great privilege and I will always be grateful to you. Finally, my supervisor and mentor, Prof. George Magoulas. Words cannot describe how grateful I am to you for all the invaluable advise, support, guidance, encouragement, feedback, hard-work and patience with me. I have learned so much from you and it has been a great honour to work with you. Thank you for not only being my supervisor but also the perfect role- model. I will always be earnestly grateful to you. 3 Table of Contents 1. Introduction 10 1.1 Research Questions 12 1.2 Methodology 13 1.3 Thesis Structure and Contribution 16 2. Behavioural Genetics inspired framework for evolving populations of neural networks: combining learning & evolution 2.1 Overview 21 2.2 Evolution and Learning and interactions therein 21 2.3 Combining Evolution and Learning using ANNs 25 2.3.1 Frameworks for combining Evolution with Learning 27 2.3.1.1 Evolving ANN connection weights 28 2.3.1.2 Evolving ANN Architectures 30 2.3.1.3 Evolving ANN Learning Rules 33 2.3.2 What is next 35 2.4 Behavioural Genetics 38 2.4.1 Genotype, Phenotype and Environment 38 2.4.2 Methods employed in BG research – twin studies & GCTA 40 2.4.3 Environment 41 2.4.4 Genetic and Environmental Influences 42 2.4.5 Heritability 44 2.4.6 Evolution and Selection 46 2.4.7 Generalist Genes, Pleiotropy and Polygenicity 48 2.5 BG as a framework for neuro-evolution 49 2.5.1 Evolutionary and Learning Task(s) 51 2.5.2 Simulating variations in genetic influences 52 2.5.2.1 Encoding structural and learning parameters into genome 52 2.5.2.2 Calibrate the range of variation in genome 53 2.5.2.3 Genotype – Phenotype Mappings 54 2.5.3 Simulating variations in environmental influences 56 2.5.3.1 Simulating shared environmental influences 56 2.5.3.2 Simulating non-shared environmental influences 57 2.5.4 Generating population of ANNs 58 2.5.5 Training and performance assessment 61 2.5.5.1 Fitness Evaluation 62 2.5.5.2 Computing Heritability 62 2.5.6 Selection 63 2.5.6.1 Roulette wheel selection (Stochastic selection) 63 2.5.6.2 Truncation selection (Deterministic selection) 64 2.5.6.3 Selection and sexual reproduction 65 2.5.6.4 Breed next generation and repeat 66 2.6 Summary and contribution of the chapter 66 3. Neuro-evolutionary framework for capturing population variability across language development: Modelling children’s past tense formation 3.1 Overview 68 3.2 An introduction to language acquisition 68 3.3 Computational modelling of past tense acquisition 73 3.4 Learning English past tense through Evolution 75 3.4.1 English past tense dataset 77 3.5 Experiment Design 78 3.5.1 How was behaviour (performance) measured 80 3.6 Roulette wheel selection based experiment results 82 3.6.1 Results and Analysis 82 3.7 Truncation Selection based experiment results 98 3.7.1 Results and Analysis 98 3.8 Analysing the effects of selection 113 4 Table of Contents 3.9 Summary and contribution of the chapter 115 4. Behavioural Genetics inspired model for Transfer Learning 4.1 Overview 117 4.2 Introduction to Transfer Learning 118 4.3 Research issues in transfer learning 121 4.3.1 What to transfer? 121 4.3.2 How to transfer? 122 4.3.3 When to transfer? 125 4.3.4 How to assess task relatedness or how to model task similarity? 125 4.4 Heterogeneous Transfer: introduction and issues 126 4.5 What is next? 127 4.6 Extending the BG inspired model to transfer learning 129 4.6.1 How to choose tasks – related or heterogeneous 131 4.6.2 Simulating neurocomputational variation (What to transfer?) 132 4.6.3 How was shared environmental variation implemented? (What to 133 transfer?) 4.6.3.1 Initial weights of ANNs as representatives of non-shared 135 environment (What not to transfer) 4.6.4 Role of using twins population (Determining task relatedness and 135 avoiding negative transfer) 4.6.5 Implementation of transfer approach (How to transfer?) 136 4.6.6 Factors affecting transfer of ‘ability to learn’ 139 4.7 Summary and contribution of the chapter 141 5.
Load more

Recommended publications
  • Volume 3 Summer 2012
    Volume 3 Summer 2012 . Academic Partners . Cover image Magnetic resonance image of the human brain showing colour-coded regions activated by smell stimulus. Editors Ulisses Barres de Almeida Max-Planck-Institut fuer Physik [email protected] Juan Rojo TH Unit, PH Division, CERN [email protected] [email protected] Academic Partners Fondazione CEUR Consortium Nova Universitas Copyright ©2012 by Associazione EURESIS The user may not modify, copy, reproduce, retransmit or otherwise distribute this publication and its contents (whether text, graphics or original research content), without express permission in writing from the Editors. Where the above content is directly or indirectly reproduced in an academic context, this must be acknowledge with the appropriate bibliographical citation. The opinions stated in the papers of the Euresis Journal are those of their respective authors and do not necessarily reflect the opinions of the Editors or the members of the Euresis Association or its sponsors. Euresis Journal (ISSN 2239-2742), a publication of Associazione Euresis, an Association for the Promotion of Scientific Endevour, Via Caduti di Marcinelle 2, 20134 Milano, Italia. www.euresisjournal.org Contact information: Email. [email protected] Tel.+39-022-1085-2225 Fax. +39-022-1085-2222 Graphic design and layout Lorenzo Morabito Technical Editor Davide PJ Caironi This document was created using LATEX 2" and X LE ATEX 2 . Letter from the Editors Dear reader, with this new issue we reach the third volume of Euresis Journal, an editorial ad- venture started one year ago with the scope of opening up a novel space of debate and encounter within the scientific and academic communities.
    [Show full text]
  • Optimization of Deep Architectures for EEG Signal Classification
    sensors Article Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms Diego Aquino-Brítez 1, Andrés Ortiz 2,* , Julio Ortega 1, Javier León 1, Marco Formoso 2 , John Q. Gan 3 and Juan José Escobar 1 1 Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain; [email protected] (D.A.-B.); [email protected] (J.O.); [email protected] (J.L.); [email protected] (J.J.E.) 2 Department of Communications Engineering, University of Málaga, 29071 Málaga, Spain; [email protected] 3 School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; [email protected] * Correspondence: [email protected] Abstract: Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architec- Citation: Aquino-Brítez, D.; Ortiz, tures include a vast number of hyperparameters on which the performance of the model relies. In this A.; Ortega, J.; León, J.; Formoso, M.; paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, Gan, J.Q.; Escobar, J.J.
    [Show full text]
  • A Python Library to Model and Analyze Diffusion Processes Over Complex Networks
    International Journal of Data Science and Analytics (2018) 5:61–79 https://doi.org/10.1007/s41060-017-0086-6 APPLICATIONS NDlib: a python library to model and analyze diffusion processes over complex networks Giulio Rossetti2 · Letizia Milli1,2 · Salvatore Rinzivillo2 · Alina Sîrbu1 · Dino Pedreschi1 · Fosca Giannotti2 Received: 19 October 2017 / Accepted: 11 December 2017 / Published online: 20 December 2017 © Springer International Publishing AG, part of Springer Nature 2017 Abstract Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib,an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians. Keywords Social network analysis software · Epidemics · Opinion dynamics 1 Introduction be modeled as networks and, as such, analyzed. Undoubt- edly, such pervasiveness has produced an amplification in the In the last decades, social network analysis, henceforth SNA, visibility of network analysis studies thus making this com- has received increasing attention from several heterogeneous plex and interesting field one of the most widespread among fields of research. Such popularity was certainly due to the higher education centers, universities and academies. Given flexibility offered by graph theory: a powerful tool that the exponential diffusion reached by SNA, several tools were allows reducing countless phenomena to a common ana- developed to make it approachable to the wider audience pos- lytical framework whose basic bricks are nodes and their sible.
    [Show full text]
  • A Neuroevolution Approach to Imitating Human-Like Play in Ms
    A Neuroevolution Approach to Imitating Human-Like Play in Ms. Pac-Man Video Game Maximiliano Miranda, Antonio A. S´anchez-Ruiz, and Federico Peinado Departamento de Ingenier´ıadel Software e Inteligencia Artificial Universidad Complutense de Madrid c/ Profesor Jos´eGarc´ıaSantesmases 9, 28040 Madrid (Spain) [email protected] - [email protected] - [email protected] http://www.narratech.com Abstract. Simulating human behaviour when playing computer games has been recently proposed as a challenge for researchers on Artificial Intelligence. As part of our exploration of approaches that perform well both in terms of the instrumental similarity measure and the phenomeno- logical evaluation by human spectators, we are developing virtual players using Neuroevolution. This is a form of Machine Learning that employs Evolutionary Algorithms to train Artificial Neural Networks by consider- ing the weights of these networks as chromosomes for the application of genetic algorithms. Results strongly depend on the fitness function that is used, which tries to characterise the human-likeness of a machine- controlled player. Designing this function is complex and it must be implemented for specific games. In this work we use the classic game Ms. Pac-Man as an scenario for comparing two different methodologies. The first one uses raw data extracted directly from human traces, i.e. the set of movements executed by a real player and their corresponding time stamps. The second methodology adds more elaborated game-level parameters as the final score, the average distance to the closest ghost, and the number of changes in the player's route. We assess the impor- tance of these features for imitating human-like play, aiming to obtain findings that would be useful for many other games.
    [Show full text]
  • Evolving Networks and Social Network Analysis Methods And
    DOI: 10.5772/intechopen.79041 ProvisionalChapter chapter 7 Evolving Networks andand SocialSocial NetworkNetwork AnalysisAnalysis Methods and Techniques Mário Cordeiro, Rui P. Sarmento,Sarmento, PavelPavel BrazdilBrazdil andand João Gama Additional information isis available atat thethe endend ofof thethe chapterchapter http://dx.doi.org/10.5772/intechopen.79041 Abstract Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in net- works during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research impor- tance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks. Keywords: evolving networks, social network analysis 1.
    [Show full text]
  • Homophily and Long-Run Integration in Social Networks∗
    Homophily and Long-Run Integration in Social Networks∗ Yann Bramoulléy Sergio Currariniz Matthew O. Jacksonx Paolo Pin{ Brian W. Rogersk January 19, 2012 Abstract We study network formation in which nodes enter sequentially and form connections through a combination of random meetings and network-based search, as in Jackson and Rogers (2007). We focus on the impact of agents' heterogeneity on link patterns when connections are formed under type-dependent biases. In particular, we are concerned with how the local neighborhood of a node evolves as the node ages. We provide a surprising general result on long-run in- tegration whereby the composition of types in a node's neighborhood approaches the global type distribution, provided that the search part of the meeting process is unbiased. Integration, however, occurs only for suciently old nodes, while the aggregate distribution of connections still reects the bias of the random process. For a special case of the model, we analyze the form of these biases with regard to type-based degree distributions and group-level homophily patterns. Finally, we illustrate aspects of the model with an empirical application to data on citations in physics journals. JEL Codes: A14, D85, I21. ∗Following the suggestion of JET editors, this paper draws from two working papers developed independently: Bramoullé and Rogers (2010) and Currarini, Jackson and Pin (2010b). We gratefully acknowledge nancial support from the NSF under grant SES-0961481 and we thank Vincent Boucher for his research assistance. We also thank Habiba Djebbari, Andrea Galeotti, Sanjeev Goyal, James Moody, Betsy Sinclair, Bruno Strulovici, and Adrien Vigier, as well as numerous seminar participants.
    [Show full text]
  • Neuro-Crítica: Un Aporte Al Estudio De La Etiología, Fenomenología Y Ética Del Uso Y Abuso Del Prefijo Neuro1
    JAHR ǀ Vol. 4 ǀ No. 7 ǀ 2013 Professional article Amir Muzur, Iva Rincic Neuro-crítica: un aporte al estudio de la etiología, fenomenología y ética del uso y abuso del prefijo neuro1 ABSTRACT The last few decades, beside being proclaimed "the decades of the brain" or "the decades of the mind," have witnessed a fascinating explosion of new disciplines and pseudo-disciplines characterized by the prefix neuro-. To the "old" specializations of neurosurgery, neurophysiology, neuropharmacology, neurobiology, etc., some new ones have to be added, which might sound somehow awkward, like neurophilosophy, neuroethics, neuropolitics, neurotheology, neuroanthropology, neuroeconomy, and other. Placing that phenomenon of "neuroization" of all fields of human thought and practice into a context of mostly unjustified and certainly too high – almost millenarianistic – expectations of the science of the brain and mind at the end of the 20th century, the present paper tries to analyze when the use of the prefix neuro- is adequate and when it is dubious. Key words: brain, neuroscience, word coinage RESUMEN Las últimas décadas, además de haber sido declaradas "las décadas del cerebro" o "las décadas de la mente", han sido testigos de una fascinante explosión de nuevas disciplinas y pseudo-disciplinas caracterizadas por el prefijo neuro-. A las "antiguas" especializaciones de la neurocirugía, la neurofisiología, la neurofarmacología, la neurobiología, etc., deben agregarse otras nuevas que pueden sonar algo extrañas, como la neurofilosofía, la neuroética, la neuropolítica, la neuroteología, la neuroantropología, la neuroeconomía, entre otras. El presente trabajo coloca ese fenómeno de "neuroización" de todos los campos del pensamiento y la práctica humana en un contexto de expectativas en general injustificadas y sin duda altísimas (y casi milenarias) en la ciencia del cerebro y la mente a fines del siglo XX; e intenta analizar cuándo el uso del prefijo neuro- es adecuado y cuándo es cuestionable.
    [Show full text]
  • Evolving Autonomous Agent Controllers As Analytical Mathematical Models
    Evolving Autonomous Agent Controllers as Analytical Mathematical Models Paul Grouchy1 and Gabriele M.T. D’Eleuterio1 1University of Toronto Institute for Aerospace Studies, Toronto, Ontario, Canada M3H 5T6 [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/alife2014/26/681/1901537/978-0-262-32621-6-ch108.pdf by guest on 27 September 2021 Abstract a significant amount of time and computational resources. Furthermore, any such design decisions introduce additional A novel Artificial Life paradigm is proposed where experimenter bias into the simulation. Finally, complex be- autonomous agents are controlled via genetically- haviors require complex representations, adding to the al- encoded Evolvable Mathematical Models (EMMs). Agent/environment inputs are mapped to agent outputs ready high computational costs of ALife algorithms while via equation trees which are evolved using Genetic Pro- further obfuscating the inner workings of the evolved agents. gramming. Equations use only the four basic mathematical We propose Evolvable Mathematical Models (EMMs), a operators: addition, subtraction, multiplication and division. novel paradigm whereby autonomous agents are evolved via Experiments on the discrete Double-T Maze with Homing GP as analytical mathematical models of behavior. Agent problem are performed; the source code has been made available. Results demonstrate that autonomous controllers inputs are mapped to outputs via a system of genetically with learning capabilities can be evolved as analytical math- encoded equations. Only the four basic mathematical op- ematical models of behavior, and that neuroplasticity and erations (addition, subtraction, multiplication and division) neuromodulation can emerge within this paradigm without are required, as they can be used to approximate any ana- having these special functionalities specified a priori.
    [Show full text]
  • Albert-László Barabási with Emma K
    Network Science Class 5: BA model Albert-László Barabási With Emma K. Towlson, Sebastian Ruf, Michael Danziger and Louis Shekhtman www.BarabasiLab.com Section 1 Introduction Section 1 Hubs represent the most striking difference between a random and a scale-free network. Their emergence in many real systems raises several fundamental questions: • Why does the random network model of Erdős and Rényi fail to reproduce the hubs and the power laws observed in many real networks? • Why do so different systems as the WWW or the cell converge to a similar scale-free architecture? Section 2 Growth and preferential attachment BA MODEL: Growth ER model: the number of nodes, N, is fixed (static models) networks expand through the addition of new nodes Barabási & Albert, Science 286, 509 (1999) BA MODEL: Preferential attachment ER model: links are added randomly to the network New nodes prefer to connect to the more connected nodes Barabási & Albert, Science 286, 509 (1999) Network Science: Evolving Network Models Section 2: Growth and Preferential Sttachment The random network model differs from real networks in two important characteristics: Growth: While the random network model assumes that the number of nodes is fixed (time invariant), real networks are the result of a growth process that continuously increases. Preferential Attachment: While nodes in random networks randomly choose their interaction partner, in real networks new nodes prefer to link to the more connected nodes. Barabási & Albert, Science 286, 509 (1999) Network Science: Evolving Network Models Section 3 The Barabási-Albert model Origin of SF networks: Growth and preferential attachment (1) Networks continuously expand by the GROWTH: addition of new nodes add a new node with m links WWW : addition of new documents PREFERENTIAL ATTACHMENT: (2) New nodes prefer to link to highly the probability that a node connects to a node connected nodes.
    [Show full text]
  • Mining Evolving Network Processes
    Mining evolving network processes Misael Mongiov`ı, Petko Bogdanov, Ambuj K. Singh Email:[email protected], [email protected], [email protected] Abstract—Processes within real world networks evolve accord- NZ Cricket Players ing to the underlying graph structure. A bounty of examples New New India India Zealand Zealand exists in diverse network genres: botnet communication growth, NCT NCT moving traffic jams [25], information foraging [37] in document NCT NCT networks (WWW and Wikipedia), and spread of viral memes Cricket Cricket or opinions in social networks. The network structure in all the Pages Pages Sri Sri above examples remains relatively fixed, while the shape, size and Pakistan Pakistan Lanka Lanka NCT NCT NCT position of the affected network regions change gradually with NCT 2011 Cricket World Cup time. Traffic jams grow, move, shrink and eventually disappear. 28/3 29/3 Finals Schedule Public attention shifts among current hot topics inducing a similar New India Zealand India shift of highly accessed Wikipedia articles. Discovery of such NCT NCT NCT smoothly evolving network processes has the potential to expose India NCT the intrinsic mechanisms of complex network dynamics, enable Cricket Cricket new data-driven models and improve network design. Pages Pages Cricket Sri Sri Pakistan Pakistan Pages Lanka Lanka We introduce the novel problem of Mining smoothly evolving NCT NCT NCT NCT processes (MINESMOOTH) in networks with dynamic real-valued node/edge weights. We show that ensuring smooth transitions in 30/3 - 31/3 1/4 - 4/4 5/4 the solution is NP-hard even on restricted network structures such as trees.
    [Show full text]
  • On Community Structure in Complex Networks: Challenges and Opportunities
    On community structure in complex networks: challenges and opportunities Hocine Cherifi · Gergely Palla · Boleslaw K. Szymanski · Xiaoyan Lu Received: November 5, 2019 Abstract Community structure is one of the most relevant features encoun- tered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we follow with an overview of the Stochastic Block Model and its different variants as well as inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over Hocine Cherifi LIB EA 7534 University of Burgundy, Esplanade Erasme, Dijon, France E-mail: hocine.cherifi@u-bourgogne.fr Gergely Palla MTA-ELTE Statistical and Biological Physics Research Group P´azm´any P. stny. 1/A, Budapest, H-1117, Hungary E-mail: [email protected] Boleslaw K. Szymanski Department of Computer Science & Network Science and Technology Center Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] Xiaoyan Lu Department of Computer Science & Network Science and Technology Center Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA E-mail: [email protected] arXiv:1908.04901v3 [physics.soc-ph] 6 Nov 2019 2 Cherifi, Palla, Szymanski, Lu the last decade.
    [Show full text]
  • Automatic Synthesis of Working Memory Neural Networks with Neuroevolution Methods Tony Pinville, Stéphane Doncieux
    Automatic synthesis of working memory neural networks with neuroevolution methods Tony Pinville, Stéphane Doncieux To cite this version: Tony Pinville, Stéphane Doncieux. Automatic synthesis of working memory neural networks with neu- roevolution methods. Cinquième conférence plénière française de Neurosciences Computationnelles, ”Neurocomp’10”, Aug 2010, Lyon, France. hal-00553407 HAL Id: hal-00553407 https://hal.archives-ouvertes.fr/hal-00553407 Submitted on 10 Mar 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AUTOMATIC SYNTHESIS OF WORKING MEMORY NEURAL NETWORKS WITH NEUROEVOLUTION METHODS Tony Pinville Stephane´ Doncieux ISIR, CNRS UMR 7222 ISIR, CNRS UMR 7222 Universite´ Pierre et Marie Curie-Paris 6 Universite´ Pierre et Marie Curie-Paris 6 4 place Jussieu, F-75252 4 place Jussieu, F-75252 Paris Cedex 05, France Paris Cedex 05, France email: [email protected] email: [email protected] ABSTRACT used in particular in Evolutionary Robotics to make real or simulated robots exhibit a desired behavior [3]. Evolutionary Robotics is a research field focused on Most evolutionary algorithms optimize a fixed size autonomous design of robots based on evolutionary algo- genotype, whereas neuroevolution methods aim at explor- rithms.
    [Show full text]