Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation

  • Alexander Mendiburu
  • Roberto Santana
  • Jose A. Lozano
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 14)


Factor graphs can serve to represent Markov networks and Bayesian networks models. They can also be employed to implement efficient inference procedures such as belief propagation. In this paper we introduce a flexible implementation of belief propagation on factor graphs in the context of estimation of distribution algorithms (EDAs). By using a transformation from Bayesian networks to factor graphs, we show the way in which belief propagation can be inserted within the Estimation of Bayesian Networks Algorithm (EBNA). The objective of the proposed variation is to increase the search capabilities by extracting information of the, computationally costly to learn, Bayesian network. Belief Propagation applied to graphs with cycles allows to find (with a low computational cost), in many scenarios, the point with the highest probability of a Bayesian network. We carry out some experiments to show how this modification can increase the potentialities of Estimation of Distribution Algorithms.


Bayesian Network Evolutionary Computation Belief Propagation Variable Node Factor Node 
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

  • Alexander Mendiburu
    • 1
  • Roberto Santana
    • 1
  • Jose A. Lozano
    • 1
  1. 1.Intelligent Systems GroupThe University of the Basque Country (UPV/EHU)San-SebastianSpain

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