Genetic Algorithms and Evolutionary Computation

Estimation of Distribution Algorithms

A New Tool for Evolutionary Computation

Editors: Larrañaga, Pedro, Lozano, Jose A (Eds.)

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About this book

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited.
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.

Table of contents (18 chapters)

  • An Introduction to Evolutionary Algorithms

    Lozano, J. A.

    Pages 3-25

  • An Introduction to Probabilistic Graphical Models

    Larrañaga, P.

    Pages 27-56

  • A Review on Estimation of Distribution Algorithms

    Larrañaga, P.

    Pages 57-100

  • Benefits of Data Clustering in Multimodal Function Optimization via EDAs

    Peña, J. M. (et al.)

    Pages 101-127

  • Parallel Estimation of Distribution Algorithms

    Lozano, J. A. (et al.)

    Pages 129-145

Buy this book

eBook $259.00
price for USA (gross)
  • ISBN 978-1-4615-1539-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $329.00
price for USA
  • ISBN 978-0-7923-7466-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $329.00
price for USA
  • ISBN 978-1-4613-5604-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

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
Series Volume
2
Copyright
2002
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4615-1539-5
DOI
10.1007/978-1-4615-1539-5
Hardcover ISBN
978-0-7923-7466-4
Softcover ISBN
978-1-4613-5604-2
Series ISSN
1568-2587
Edition Number
1
Number of Pages
XXXIV, 382
Topics