Learning Classifier Systems

1. Front Matter

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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

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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.

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

• 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

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