Advances in Learning Classifier Systems

Pier L. Lanzi

ISBN 10: 3540424377 / ISBN 13: 9783540424376

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2001, 2001

Riguardo questo articolo

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch.

Codice articolo 9783540424376

Dati bibliografici

• Titolo: Advances in Learning Classifier Systems
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2001
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 3540424377
• ISBN 13: 9783540424376
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 16 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 16 millimetri di profondità
• Peso: 441 grammi

Descrizione articolo

Riassunto

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.

Contenuti

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.