Learning Classifier Systems

5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002. Revised Papers

  • Pier Luca Lanzi
  • Wolfgang Stolzmann
  • Stewart W. Wilson
Conference proceedings IWLCS 2002

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2661)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2661)

Table of contents

  1. Front Matter
  2. Phillip William Dixon, Dawid Wolfe Corne, Martin John Oates
    Pages 20-29
  3. Gilles Enée, Pierre Barbaroux
    Pages 30-45
  4. Tim Kovacs
    Pages 61-80
  5. Tim Kovacs
    Pages 81-98
  6. Samuel Landau, Sébastien Picault, Olivier Sigaud, Pierre Gérard
    Pages 99-117
  7. Xavier Llorà, David E. Goldberg, Ivan Traus, Ester Bernadó
    Pages 118-142
  8. Patrícia A. Vargas, Leandro N. de Castro, Fernando J. Von Zuben
    Pages 163-186
  9. Back Matter

About these proceedings


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

Editors and affiliations

  • Pier Luca Lanzi
    • 1
  • Wolfgang Stolzmann
    • 2
  • Stewart W. Wilson
    • 3
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  2. 2.Daimler Chrysler AGSindelfingenGermany
  3. 3.Prediction Dynamics, Concord MA 01742 USA, Department of General EngineeringThe University of Illinois at Urbana-ChampaignUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-20544-9
  • Online ISBN 978-3-540-40029-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site