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Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation

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Markov Networks in Evolutionary Computation

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 14))

Abstract

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.

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Authors and Affiliations

  1. Intelligent Systems Group, The University of the Basque Country (UPV/EHU), San-Sebastian, Spain

    Alexander Mendiburu, Roberto Santana & Jose A. Lozano

Authors
  1. Alexander Mendiburu
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  2. Roberto Santana
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  3. Jose A. Lozano
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Correspondence to Alexander Mendiburu .

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Editors and Affiliations

  1. Transformation Practice, BT Innovate & Design, Business Modelling and Operational, Cauldwell Hall Road 172, Ipswich, IP4 5DB, United Kingdom

    Siddhartha Shakya

  2. Intelligent Systems Group, Faculty of Informatics, University of the Basque Country, Boadilla del Monte, San Sebastian, Spain

    Roberto Santana

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Mendiburu, A., Santana, R., Lozano, J.A. (2012). Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation. In: Shakya, S., Santana, R. (eds) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28900-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-28900-2_9

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