Advances in Learning Classifier Systems Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers
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 r...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2001, 2001
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| Edition: | 1st ed. 2001 |
| Series: | Lecture Notes in Artificial Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Theory
- An Artificial Economy of Post Production Systems
- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness
- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks
- Probability-Enhanced Predictions in the Anticipatory Classifier System
- YACS: Combining Dynamic Programming with Generalization in Classifier Systems
- A Self-Adaptive Classifier System
- What Makes a Problem Hard for XCS?
- Applications
- Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database
- Strength and Money: An LCS Approach to Increasing Returns
- Using Classifier Systems as Adaptive Expert Systems for Control
- Mining Oblique Data with XCS
- Advanced Architectures
- A Study on the Evolution of Learning Classifier Systems
- Learning Classifier Systems Meet Multiagent Environments
- The Bibliography
- A Bigger Learning Classifier Systems Bibliography
- An Algorithmic Description of XCS.