Learning Classifier Systems

International Workshops, IWLCS 2003-2005, Revised Selected Papers

  • Editors
  • Tim Kovacs
  • Xavier Llorà
  • Keiki Takadama
  • Pier Luca Lanzi
  • Wolfgang Stolzmann
  • Stewart W. Wilson

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4399)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4399)

Table of contents

  1. Front Matter
  2. Knowledge Representation

    1. Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai
      Pages 1-16
    2. Olgierd Unold, Grzegorz Dabrowski
      Pages 17-24
    3. Xavier Llorà, Kumara Sastry, David E. Goldberg
      Pages 40-58
  3. Mechanisms

    1. Flavio Baronti, Alessandro Passaro, Antonina Starita
      Pages 80-92
    2. Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, Da-qian Gu
      Pages 93-103
    3. Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
      Pages 104-114
    4. Ali Hamzeh, Adel Rahmani
      Pages 115-127
    5. Atsushi Wada, Keiki Takadama, Katsunori Shimohara
      Pages 128-143
    6. Samuel Landau, Olivier Sigaud, Sébastien Picault, Pierre Gérard
      Pages 144-160
    7. Albert Orriols-Puig, Ester Bernadó-Mansilla
      Pages 161-180
    8. John H. Holmes, Jennifer A. Sager, Warren B. Bilker
      Pages 181-192
  4. New Directions

  5. Application-Oriented Research and Tools

    1. Jaume Bacardit, Martin V. Butz
      Pages 282-290

About these proceedings

Introduction

The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.

Keywords

Fuzzy adaptive exploration rate algorithmic learning algorithms complexity constraints data mining decision trees evolutionary algorithms function approximation fuzzy sets genetic algorithm knowledge representation learning proving

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-71231-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-71230-5
  • Online ISBN 978-3-540-71231-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book