Advances In Learning Classifier Systems
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Advances In Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher: Springer
Release Date : 2003-07-31
Advances In Learning Classifier Systems written by Pier L. Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-31 with Computers categories.
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.
Advances In Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher: Springer
Release Date : 2001-08-29
Advances In Learning Classifier Systems written by Pier L. Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-29 with Computers categories.
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.
Advances In Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher:
Release Date : 2014-01-15
Advances In Learning Classifier Systems written by Pier L. Lanzi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Learning Classifier Systems
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Author : Pier Luca Lanzi
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-24
Learning Classifier Systems written by Pier Luca Lanzi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-24 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII. The 10 revised full papers presented together with a comprehensive bibliography on learning classifier systems were carefully reviewed and selected during two rounds of refereeing and improvement. All relevant issues in the area are addressed.
Learning Classifier Systems
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Author : Jaume Bacardit
language : en
Publisher: Springer
Release Date : 2008-10-17
Learning Classifier Systems written by Jaume Bacardit and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-17 with Computers categories.
This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.
Advances In Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher: Springer
Release Date : 2003-08-01
Advances In Learning Classifier Systems written by Pier L. Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001. The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, stock trading, and power distributionn networks.
Learning Classifier Systems
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Author : Tim Kovacs
language : en
Publisher: Springer
Release Date : 2007-06-11
Learning Classifier Systems written by Tim Kovacs and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-11 with Computers categories.
This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
Applications Of Learning Classifier Systems
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Author : Larry Bull
language : en
Publisher: Springer
Release Date : 2012-08-13
Applications Of Learning Classifier Systems written by Larry Bull and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-13 with Computers categories.
The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older "standard Af' . One focus was what Holland called "classifier systems": sets of competing rule like "classifiers", each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule "popu lation" would explore its available actions and responses, rewarding and rating the active rules accordingly. Then "good" classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.
Foundations Of Learning Classifier Systems
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Author : Larry Bull
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-07-22
Foundations Of Learning Classifier Systems written by Larry Bull and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-22 with Computers categories.
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
Genetic And Evolutionary Computation Gecco 2004
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Author : Kalyanmoy Deb
language : en
Publisher: Springer
Release Date : 2004-06-01
Genetic And Evolutionary Computation Gecco 2004 written by Kalyanmoy Deb and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-01 with Computers categories.
The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.