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Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks

  • Hossein Karshenas
  • Roberto Santana
  • Concha Bielza
  • Pedro Larrañaga
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 14)

Abstract

Because of their intrinsic properties, the majority of the estimation of distribution algorithms proposed for continuous optimization problems are based on the Gaussian distribution assumption for the variables. This paper looks over the relation between the general multivariate Gaussian distribution and the popular undirected graphical model of Markov networks and discusses how they can be employed in estimation of distribution algorithms for continuous optimization. A number of learning and sampling techniques for thesemodels, including the promising regularized model learning, are also reviewed and their application for function optimization in the context of estimation of distribution algorithms is studied.

Keywords

Multivariate Gaussian Distribution Multivariate Gaussian Distribution Distribution Algorithm Markov Network Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Hossein Karshenas
    • 1
  • Roberto Santana
    • 2
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Computational Intelligence Group, Faculty of InformaticsTechnical University of MadridMadridSpain
  2. 2.Intelligent Systems Group, Faculty of InformaticsUniversity of the Basque Country (UPV/EHU)San-SebastianSpain

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