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Simulated Evolution and Learning

7th International Conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008, Proceedings

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Xiaodong Li

    1. School of Computer Science and information Technology, RMIT University, Melbourne, Australia

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  2. Michael Kirley

    1. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia

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  3. Mengjie Zhang

    1. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand

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  4. David Green

    1. Faculty of Information Technology, Monash University, Australia

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  5. Vic Ciesielski

    1. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

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  6. Hussein Abbass

    1. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

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  7. Zbigniew Michalewicz

    1. School of Computer Science, University of Adelaide, Adelaide, Australia

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  8. Tim Hendtlass

    1. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

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  9. Kalyanmoy Deb

    1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, India

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  10. Kay Chen Tan

    1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore

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  11. Jürgen Branke

    1. Institute AIFB, University of Karlsruhe, Karlsruhe, Germany

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  12. Yuhui Shi

    1. Xi’an Jiaotong-Liverpool University, Suzhoum, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5361)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: SEAL 2008.

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Table of contents (65 papers)

  1. Front Matter

    Download chapter PDF

  2. Evolutionary Learning

    1. Modelling Behaviour Cycles for Life-Long Learning in Motivated Agents

      • Kathryn Merrick
      Pages 1-10

    2. Breaking the Synaptic Dogma: Evolving a Neuro-inspired Developmental Network

      • Gul Muhammad Khan, Julian F. Miller, David M. Halliday
      Pages 11-20

    3. A New Approach to Adapting Control Parameters in Differential Evolution Algorithm

      • Liang Feng, Yin-Fei Yang, Yu-Xuan Wang
      Pages 21-30

    4. A Novel Genetic Algorithm with Orthogonal Prediction for Global Numerical Optimization

      • Jun Zhang, Jing-Hui Zhong, Xiao-Min Hu
      Pages 31-40

    5. Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation

      • Md. Rafiul Hassan, M. Maruf Hossain, C. K. Karmakar, Michael Kirley
      Pages 41-50

    6. Evolved Look-Up Tables for Simulated DNA Controlled Robots

      • Gary Greenfield
      Pages 51-60

    7. Multi-objective Improvement of Software Using Co-evolution and Smart Seeding

      • Andrea Arcuri, David Robert White, John Clark, Xin Yao
      Pages 61-70

    8. Policy Evolution with Grammatical Evolution

      • Yow Tzu Lim, Pau Chen Cheng, John Andrew Clark, Pankaj Rohatgi
      Pages 71-80

    9. A PSO Based Adaboost Approach to Object Detection

      • Ammar W. Mohemmed, Mengjie Zhang, Mark Johnston
      Pages 81-90

    10. Adaptive Non-uniform Distribution of Quantum Particles in mQSO

      • Krzysztof Trojanowski
      Pages 91-100

    11. Genetically Evolved Fuzzy Rule-Based Classifiers and Application to Automotive Classification

      • Teck Wee Chua, Woei Wan Tan
      Pages 101-110

    12. Improving XCS Performance by Distribution

      • Urban Richter, Holger Prothmann, Hartmut Schmeck
      Pages 111-120

    13. Evolving an Ensemble of Neural Networks Using Artificial Immune Systems

      • Bruno H. G. Barbosa, Lam T. Bui, Hussein A. Abbass, Luis A. Aguirre, Antônio P. Braga
      Pages 121-130

    14. Improving the Performance and Scalability of Differential Evolution

      • Antony W. Iorio, Xiaodong Li
      Pages 131-140

    15. A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management

      • Cassandra X. H. Tang, Henry C. W. Lau
      Pages 141-150

  3. Evolutionary Optimisation

    1. Solving the Delay-Constrained Capacitated Minimum Spanning Tree Problem Using a Dandelion-Encoded Evolutionary Algorithm

      • Ángel M. Pérez-Bellido, Sancho Salcedo-Sanz, Emilio G. Ortiz-García, Antonio Portilla-Figueras, Maurizio Naldi
      Pages 151-160

    2. Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem

      • Piotr Switalski, Franciszek Seredynski
      Pages 161-169

    3. Improving NSGA-II Algorithm Based on Minimum Spanning Tree

      • Miqing Li, Jinhua Zheng, Jun Wu
      Pages 170-179

    4. An Island Based Hybrid Evolutionary Algorithm for Optimization

      • Changhe Li, Shengxiang Yang
      Pages 180-189
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Other volumes

  1. Simulated Evolution and Learning

Keywords

About this book

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.

Editors and Affiliations

  • School of Computer Science and information Technology, RMIT University, Melbourne, Australia

    Xiaodong Li

  • Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia

    Michael Kirley

  • School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  • Faculty of Information Technology, Monash University, Australia

    David Green

  • School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Vic Ciesielski

  • School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  • School of Computer Science, University of Adelaide, Adelaide, Australia

    Zbigniew Michalewicz

  • Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  • Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, India

    Kalyanmoy Deb

  • Department of Electrical and Computer Engineering, National University of Singapore, Singapore

    Kay Chen Tan

  • Institute AIFB, University of Karlsruhe, Karlsruhe, Germany

    Jürgen Branke

  • Xi’an Jiaotong-Liverpool University, Suzhoum, China

    Yuhui Shi

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