Estimation of Distribution Algorithms

A New Tool for Evolutionary Computation

  • Pedro Larrañaga
  • Jose A. Lozano

Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 2)

Table of contents

  1. Front Matter
    Pages i-xxxiv
  2. Foundations

    1. Front Matter
      Pages 3-3
    2. J. M. Peña, J. A. Lozano, P. Larrañaga
      Pages 101-127
    3. J. A. Lozano, R. Sagarna, P. Larrañaga
      Pages 129-145
    4. C. González, J. A. Lozano, P. Larrañaga
      Pages 147-163
  3. Optimization

    1. Front Matter
      Pages 165-165
    2. E. Bengoetxea, T. Miquélez, P. Larrañaga, J. A. Lozano
      Pages 181-194
    3. R. Sagarna, P. Larrañaga
      Pages 195-209
    4. V. Robles, P. de Miguel, P. Larrañaga
      Pages 211-229
    5. E. Bengoetxea, P. Larrañaga, A. Perchant, I. Bloch
      Pages 243-265
  4. Machine Learning

    1. Front Matter
      Pages 267-267
    2. I. Inza, P. Larrañaga, B. Sierra
      Pages 269-293
    3. B. Sierra, E. A. Jiménez, I. Inza, P. Larrañaga, J. Muruzábal
      Pages 313-322
    4. L. M. Campos, J. A. Gámez, P. Larrañaga, S. Moral, T. Romero
      Pages 323-341
    5. J. Roure, P. Larrañaga, R. Sangüesa
      Pages 343-360
    6. C. Cotta, E. Alba, R. Sagarna, P. Larrañaga
      Pages 361-377
  5. Back Matter
    Pages 379-382

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.


Bayesian network Cluster algorithms evolutionary algorithm genetic algorithms k-means learning machine learning modeling optimization

Editors and affiliations

  • Pedro Larrañaga
    • 1
  • Jose A. Lozano
    • 1
  1. 1.University of the Basque CountrySpain

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5604-2
  • Online ISBN 978-1-4615-1539-5
  • Series Print ISSN 1568-2587
  • Buy this book on publisher's site