Bacardit / Bernadó-Mansilla / Butz 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
2008
ISBN: 978-3-540-88138-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 4998, 307 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Bacardit, Jaume

Bernadó-Mansilla, Ester

Butz, Martin V.

Kovacs, Tim

Llorà, Xavier

Takadama, Keiki

Inhaltsverzeichnis

Learning Classifier Systems: Looking Back and Glimpsing Ahead.- Knowledge Representations.- Analysis of Population Evolution in Classifier Systems Using Symbolic Representations.- Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets.- Evolving Fuzzy Rules with UCS: Preliminary Results.- Analysis of the System.- A Principled Foundation for LCS.- Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS.- Mechanisms.- Analysis and Improvements of the Classifier Error Estimate in XCSF.- A Learning Classifier System with Mutual-Information-Based Fitness.- On Lookahead and Latent Learning in Simple LCS.- A Learning Classifier System Approach to Relational Reinforcement Learning.- Linkage Learning, Rule Representation, and the ?-Ary Extended Compact Classifier System.- New Directions.- Classifier Conditions Using Gene Expression Programming.- Evolving Classifiers Ensembles with Heterogeneous Predictors.- Substructural Surrogates for Learning Decomposable Classification Problems.- Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System.- Applications.- Technology Extraction of Expert Operator Skills from Process Time Series Data.- Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks.