Advances in Learning Classifier Systems

Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers

By: Pier L. Lanzi (Editor), Wolfgang Stolzmann (Editor), Stewart W. Wilson (Editor)

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Paperback | 29 August 2001

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Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

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Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Medicine Medicine in General Artificial Intelligence Machine Learning Mathematics Mathematical Foundation Mathematical Logic

Theory
An Artificial Economy of Post Production Systems	p. 3
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness	p. 21
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks	p. 29
Probability-Enhanced Predictions in the Anticipatory Classifier System	p. 37
YACS: Combining Dynamic Programming with Generalization in Classifier Systems	p. 52
A Self-Adaptive Classifier System	p. 70
What Makes a Problem Hard for XCS?	p. 80
Applications
Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database	p. 103
Strength and Money: An LCS Approach to Increasing Returns	p. 114
Using Classifier Systems as Adaptive Expert Systems for Control	p. 138
Mining Oblique Data with XCS	p. 158
Advanced Architectures
A Study on the Evolution of Learning Classifier Systems	p. 177
Learning Classifier Systems Meet Multiagent Environments	p. 192
The Bibliography
A Bigger Learning Classifier Systems Bibliography	p. 213
Appendix
An Algorithmic Description of XCS	p. 253
Author Index	p. 273
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Advances in Learning Classifier Systems

Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers

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Paperback

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