Learning Classifier Systems 11th International Workshop, IWLCS 2
This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, and in Montreal, Canada, in July 2009 - all hosted
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Subseries of Lecture Notes in Computer Science
6471
Jaume Bacardit Will Browne Jan Drugowitsch Ester Bernadó-Mansilla Martin V. Butz (Eds.)
Learning Classifier Systems 11th International Workshop, IWLCS 2008 Atlanta, GA, USA, July 13, 2008 and 12th International Workshop, IWLCS 2009 Montreal, QC, Canada, July 9, 2009 Revised Selected Papers
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Jaume Bacardit University of Nottingham, Nottingham, NG8 1BB, UK E-mail: [email protected] Will Browne Victoria University of Wellington, Wellington 6140, New Zealand E-mail: [email protected] Jan Drugowitsch University of Rochester, Rochester, NY 14627, USA E-mail: [email protected] Ester Bernadó-Mansilla Universitat Ramon Llull, 08022 Barcelona, Spain E-mail: [email protected] Martin V. Butz University of Würzburg, 97070 Würzburg, Germany E-mail: [email protected]
Library of Congress Control Number: 2010940267
CR Subject Classification (1998): I.2.6, I.2, H.3, D.2.4, D.2.8, F.1, H.4, H.2.8 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-642-17507-4 Springer Berlin Heidelberg New York 978-3-642-17507-7 Springer Berlin Heidelberg New York
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Preface
Learning Classifier Systems (LCS) constitute a fascinating concept at the intersection of machine learning and evolutionary computation. LCS’s genetic search, generally in combination with reinforcement learning techniques, can be applied to both temporal and spatial problem-solving and promotes powerful search in a wide variety of domains. The LCS concept allows many representations of the learned knowledge from simple production rules to artificial neural networks to linear approximations often in a human readable form. The concepts underlying LCS have been developed for over 30 years, with the annual International Workshop on Learning Classifier Systems supporting the field since 1992. From 1999 onwards the workshop has been held yearly, in conjunction with PPSN in 2000 and 2002 and with GECCO in 1999, 2001, and from 2003 onwards. This book is
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