Simulated Evolution and Learning

Xiaodong Li

ISBN 10: 3540896937 / ISBN 13: 9783540896937

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2008, 2008

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Neuware -This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7¿10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 676 pp. Englisch.

Codice articolo 9783540896937

Dati bibliografici

• Titolo: Simulated Evolution and Learning
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2008
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540896937
• ISBN 13: 9783540896937
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

This volume constitutes the proceedings of the 7th International Conference on Simulated Evolution and Learning, SEAL 2008, held in Melbourne, Australia, during December 7-10, 2008. The 65 papers presented were carefully reviewed and selected from 140 submissions. The topics covered are evolutionary learning; evolutionary optimisation; hybrid learning; adaptive systems; theoretical issues in evolutionary computation; and real-world applications of evolutionary computation techniques.