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Advances in Learning Classifier Systems

Third International Workshop, IWLCS 2000 Paris, France, September 15–16, 2000 Revised Papers

  • Pier Luca Lanzi
  • Wolfgang Stolzmann
  • Stewart W. Wilson
Conference proceedings IWLCS 2000

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1996)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 1996)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Theory

  3. Applications

  4. Advanced Architectures

    1. Tiago Sepúlveda, Mário Rui Gomes
      Pages 177-191
    2. Keiki Takadama, Takao Terano, Katsunori Shimohara
      Pages 192-210
  5. The Bibliography

    1. Tim Kovacs, Pier Luca Lanzi
      Pages 213-249
  6. Appendix

    1. Martin V. Butz, Stewart W. Wilson
      Pages 253-272
  7. Back Matter
    Pages 273-273

About these proceedings

Introduction

Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

Keywords

Adaptation Algorithmic Learning Classification Classifier Systems Clustering Data Analysis Data Mining Evolutionary Computing Learning Classifier Systems Multiagent Learning Rule-Based Systems algorithms expert system genetic algorithms learning

Editors and affiliations

  • Pier Luca Lanzi
    • 1
  • Wolfgang Stolzmann
    • 2
  • Stewart W. Wilson
    • 3
  1. 1.Politecnico di Milano Dipartimento di Elettronica e InformazioneArtificial Intelligence and Robotics LaboratoryMilanItaly
  2. 2.DaimlerChrysler AG Research and Technology,Cognition and RoboticsBerlinGermany
  3. 3.Prediction DynamicsConcordUSA

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-44640-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42437-6
  • Online ISBN 978-3-540-44640-8
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site