close
Skip to main content
Springer Nature Link
Log in

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

  • Conference proceedings
  • © 2008
Save conference proceedings
View saved research

Overview

Editors:
  1. Jaume Bacardit

    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 5RD, UK

  2. Ester Bernadó-Mansilla

    1. Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, Barcelona, Spain

  3. Martin V. Butz

    1. Department of Psychology, University of Würzburg, Würzburg, Germany

  4. Tim Kovacs

    1. Department of Computer Science, University of Bristol, Bristol, UK

  5. Xavier Llorà

    1. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, Urbana, USA

  6. Keiki Takadama

    1. Tokyo Institute of Technology, tokyo, Japan

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4998)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: IWLCS 2006. IWLCS 2007.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

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.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects.

Table of contents (17 papers)

  1. Front Matter

    Download chapter PDF

  2. Introduction

    1. Learning Classifier Systems: Looking Back and Glimpsing Ahead

      • Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz
      Pages 1-21

  3. Knowledge Representations

    1. Analysis of Population Evolution in Classifier Systems Using Symbolic Representations

      • Pier Luca Lanzi, Stefano Rocca, Kumara Sastry, Stefania Solari
      Pages 22-45

    2. Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets

      • Charalambos Ioannides, Will Browne
      Pages 46-56

    3. Evolving Fuzzy Rules with UCS: Preliminary Results

      • Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla
      Pages 57-76

  4. Analysis of the System

    1. A Principled Foundation for LCS

      • Jan Drugowitsch, Alwyn M. Barry
      Pages 77-95

    2. Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS

      • Albert Orriols-Puig, Ester Bernadó-Mansilla
      Pages 96-116

  5. Mechanisms

    1. Analysis and Improvements of the Classifier Error Estimate in XCSF

      • Daniele Loiacono, Jan Drugowitsch, Alwyn Barry, Pier Luca Lanzi
      Pages 117-135

    2. A Learning Classifier System with Mutual-Information-Based Fitness

      • Robert Elliott Smith, Max Kun Jiang
      Pages 136-153

    3. On Lookahead and Latent Learning in Simple LCS

      • Larry Bull
      Pages 154-168

    4. A Learning Classifier System Approach to Relational Reinforcement Learning

      • Drew Mellor
      Pages 169-188

    5. Linkage Learning, Rule Representation, and the χ-Ary Extended Compact Classifier System

      • Xavier Llorà, Kumara Sastry, Cláudio F. Lima, Fernando G. Lobo, David E. Goldberg
      Pages 189-205

  6. New Directions

    1. Classifier Conditions Using Gene Expression Programming

      • Stewart W. Wilson
      Pages 206-217

    2. Evolving Classifiers Ensembles with Heterogeneous Predictors

      • Pier Luca Lanzi, Daniele Loiacono, Matteo Zanini
      Pages 218-234

    3. Substructural Surrogates for Learning Decomposable Classification Problems

      • Albert Orriols-Puig, Kumara Sastry, David E. Goldberg, Ester Bernadó-Mansilla
      Pages 235-254

    4. Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System

      • Jaume Bacardit, Natalio Krasnogor
      Pages 255-268

  7. Applications

    1. Technology Extraction of Expert Operator Skills from Process Time Series Data

      • Setsuya Kurahashi, Takao Terano
      Pages 269-285

    2. Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks

      • Renan C. Moioli, Patricia A. Vargas, Fernando J. Von Zuben
      Pages 286-305

  8. Back Matter

    Download chapter PDF
Back to top

Other volumes

  1. Learning Classifier Systems

Editors and Affiliations

  • 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 5RD, UK

    Jaume Bacardit

  • Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, Barcelona, Spain

    Ester Bernadó-Mansilla

  • Department of Psychology, University of Würzburg, Würzburg, Germany

    Martin V. Butz

  • Department of Computer Science, University of Bristol, Bristol, UK

    Tim Kovacs

  • Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, Urbana, USA

    Xavier Llorà

  • Tokyo Institute of Technology, tokyo, Japan

    Keiki Takadama

Accessibility Information

Accessibility information for this book is coming soon. We're working to make it available as quickly as possible. Thank you for your patience.

Bibliographic Information

Keywords

Publish with us

Policies and ethics

Back to top