Learning Classifier Systems

From Foundations to Applications

  • Pier Luca Lanzi
  • Wolfgang Stolzmann
  • Stewart W. Wilson
Conference proceedings IWLCS 1999

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 1813)

Table of contents

  1. Front Matter
    Pages I-X
  2. Basics

    1. John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg, Stephanie Forrest et al.
      Pages 3-32
    2. Stewart W. Wilson
      Pages 63-81
  3. Advanced Topics

    1. Andrea Bonarini, Claudio Bonacina, Matteo Matteucci
      Pages 107-124
    2. Wolfgang Stolzmann
      Pages 175-194
    3. Andy Tomlinson, Larry Bull
      Pages 195-208
    4. Stewart W. Wilson
      Pages 209-219
  4. Applications

    1. Shaun Saxon, Alwyn Barry
      Pages 223-242
    2. Sonia Schulenburg, Peter Ross
      Pages 263-282
    3. Robert E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, R. K. Mehra
      Pages 283-300
  5. The Bibliography

    1. Tim Kovacs, Pier Luca Lanzi
      Pages 321-347
  6. Back Matter
    Pages 349-349

About these proceedings


Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.


Extension agents algorithmic learning algorithms autonomous robot data mining evolution fuzzy genetic algorithms knowledge knowledge discovery learning machine learning robot robotics

Editors and affiliations

  • Pier Luca Lanzi
    • 1
  • Wolfgang Stolzmann
    • 2
  • Stewart W. Wilson
    • 3
    • 4
  1. 1.Dipartimento di Elettronica ed InformatzionePolitecnico di MilanoMilanoItaly
  2. 2.Institut für Psychologie IIIUniversität WürzburgWürzburgGermany
  3. 3.Prediction DynamicsConcordUSA
  4. 4.Department of General EngineeringUniversity of Illinois at Urbana-ChampaignUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2000
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-67729-1
  • Online ISBN 978-3-540-45027-6
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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