Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001, San Francisco, CA, USA, July 7-8, 2001. Revised Papers, Volume 4

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Springer Science & Business Media, Jun 12, 2002 - Computers - 229 pages
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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.
 

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Contents

Biasing Exploration in an Anticipatory Learning Classifier System
3
An Incremental Multiplexer Problem and Its Uses in Classifier System Research
23
A Minimal Model of Communication for a Multiagent Classifier System
32
A Representation for AccuracyBased Assessment of Classifier System Prediction Performance
43
A SelfAdaptive XCS
57
Two Views of Classifier Systems
74
The Emergence of Vacillating Behaviour in the El Farol Bar Problem
88
A Comparative Study of Two Learning Classifier Systems on Data Mining
115
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
133
Explorations in LCS Models of Stock Trading
151
OnLine Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems
181
Compact Rulesets from XCSI
197
An Algorithmic Description of ACS2
211
Author Index
230
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About the author (2002)

Stewart W. Wilson is a scientist at The Rowland Institute for Science, Cambridge, Massachusetts.