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Table of contents (18 chapters)
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Front Matter
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Foundations
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Front Matter
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Optimization
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Front Matter
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Machine Learning
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Front Matter
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Keywords
About this book
This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks.
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science.
`... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.
Editors and Affiliations
Bibliographic Information
Book Title: Estimation of Distribution Algorithms
Book Subtitle: A New Tool for Evolutionary Computation
Editors: Pedro Larrañaga, Jose A. Lozano
Series Title: Genetic Algorithms and Evolutionary Computation
DOI: https://doi.org/10.1007/978-1-4615-1539-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2002
Hardcover ISBN: 978-0-7923-7466-4Published: 31 October 2001
Softcover ISBN: 978-1-4613-5604-2Published: 30 October 2012
eBook ISBN: 978-1-4615-1539-5Published: 06 December 2012
Series ISSN: 1568-2587
Edition Number: 1
Number of Pages: XXXIV, 382
Topics: Software Engineering/Programming and Operating Systems, Artificial Intelligence, Programming Languages, Compilers, Interpreters