Advances in Learning Classifier Systems

Pier L. Lanzi

ISBN 10: 3540437932 / ISBN 13: 9783540437932

Published by Springer, Springer Jun 2002, 2002

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Theory.- Biasing Exploration in an Anticipatory Learning Classifier System.- An Incremental Multiplexer Problem and Its Uses in Classifier System Research.- A Minimal Model of Communication for a Multi-agent Classifier System.- A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance.- A Self-Adaptive XCS.- Two Views of Classifier Systems.- Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the 'El Farol' Bar Problem.- Applications.- XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining.- A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool.- Explorations in LCS Models of Stock Trading.- On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems.- Compact Rulesets from XCSI.- An Algorithmic Description of ACS2.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 244 pp. Englisch.

Seller Inventory # 9783540437932

Bibliographic Details

• Title: Advances in Learning Classifier Systems
• Publisher: Springer, Springer Jun 2002
• Publication Date: 2002
• Language: English
• ISBN 10: 3540437932
• ISBN 13: 9783540437932
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 14D millimeters 235 millimeters width by 155 millimeters height by 14 millimeters depth
• Weight: 376 grams

About this title

Synopsis

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001.
The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, stock trading, and power distributionn networks.