Learning Classifier Systems

10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006 and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers

  • Jaume Bacardit
  • Ester Bernadó-Mansilla
  • Martin V. Butz
  • Tim Kovacs
  • Xavier Llorà
  • Keiki Takadama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)

Table of contents

  1. Front Matter
  2. Introduction

    1. Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz
      Pages 1-21
  3. Knowledge Representations

    1. Pier Luca Lanzi, Stefano Rocca, Kumara Sastry, Stefania Solari
      Pages 22-45
    2. Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
      Pages 57-76
  4. Analysis of the System

    1. Jan Drugowitsch, Alwyn M. Barry
      Pages 77-95
    2. Albert Orriols-Puig, Ester Bernadó-Mansilla
      Pages 96-116
  5. Mechanisms

    1. Daniele Loiacono, Jan Drugowitsch, Alwyn Barry, Pier Luca Lanzi
      Pages 117-135
    2. Robert Elliott Smith, Max Kun Jiang
      Pages 136-153
    3. Xavier Llorà, Kumara Sastry, Cláudio F. Lima, Fernando G. Lobo, David E. Goldberg
      Pages 189-205
  6. New Directions

    1. Pier Luca Lanzi, Daniele Loiacono, Matteo Zanini
      Pages 218-234
    2. Albert Orriols-Puig, Kumara Sastry, David E. Goldberg, Ester Bernadó-Mansilla
      Pages 235-254
  7. Applications

    1. Renan C. Moioli, Patricia A. Vargas, Fernando J. Von Zuben
      Pages 286-305
  8. Back Matter

About these proceedings

Introduction

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO.

The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Keywords

algorithmic learning approximation complexity constraints evolution evolutionary computation knowledge knowledge discovery knowledge representation learning optimization population evolution robotic rule representation vagueness

Editors and affiliations

  • Jaume Bacardit
    • 1
  • Ester Bernadó-Mansilla
    • 2
  • Martin V. Butz
    • 3
  • Tim Kovacs
    • 4
  • Xavier Llorà
    • 5
  • Keiki Takadama
    • 6
  1. 1.University of Nottingham, School of Computer Science, ASAP research group, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RDUK
  2. 2.Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2Universitat Ramon LlullBarcelonaSpain
  3. 3.Department of PsychologyUniversity of WürzburgWürzburgGermany
  4. 4.Department of Computer ScienceUniversity of BristolBristolUK
  5. 5.Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL)University of Illinois at Urbana-ChampaignUrbanaUSA
  6. 6.Tokyo Institute of TechnologytokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-88138-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-88137-7
  • Online ISBN 978-3-540-88138-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349