Learning Classifier Systems

1. Front Matter

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2. Balancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System

   • William N. L. Browne

   Pages 1-19

3. A Ruleset Reduction Algorithm for the XCS Learning Classifier System

   • Phillip William Dixon, Dawid Wolfe Corne, Martin John Oates

   Pages 20-29

4. Adapted Pittsburgh-Style Classifier-System: Case-Study

   • Gilles Enée, Pierre Barbaroux

   Pages 30-45

5. The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance

   • John H. Holmes, Warren B. Bilker

   Pages 46-60

6. XCS’s Strength-Based Twin: Part I

   • Tim Kovacs

   Pages 61-80

7. XCS’s Strength-Based Twin: Part II

   • Tim Kovacs

   Pages 81-98

8. Further Comparison between ATNoSFERES and XCSM

   • Samuel Landau, Sébastien Picault, Olivier Sigaud, Pierre Gérard

   Pages 99-117

9. Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection

   • Xavier Llorà, David E. Goldberg, Ivan Traus, Ester Bernadó

   Pages 118-142

10. Anticipatory Classifier System Using Behavioral Sequences in Non-Markov Environments

    • Marc Métivier, Claude Lattaud

    Pages 143-162

11. Mapping Artificial Immune Systems into Learning Classifier Systems

    • Patrícia A. Vargas, Leandro N. de Castro, Fernando J. Von Zuben

    Pages 163-186

12. The 2003 Learning Classifier Systems Bibliography

    • Tim Kovacs

    Pages 187-229

13. Back Matter

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The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.

• adaptive classifiert systems
• adaptive learning
• algorithmic learning
• algorithms
• classification
• clustering
• data mining
• discovery science
• evolutionary learning
• knowledge discovery
• learning
• learning classifier systems
• multi-agent learning
• rule discovery

• Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

  Pier Luca Lanzi

• Daimler Chrysler AG, Sindelfingen, Germany

  Wolfgang Stolzmann

• Prediction Dynamics, Concord MA 01742 USA, Department of General Engineering, The University of Illinois at Urbana-Champaign, USA

  Stewart W. Wilson

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