Introducing belief propagation in estimation of distribution algorithms: A parallel framework

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“Introducing belief propagation in estimation of distribution algorithms: A parallel framework” by A. Mendiburu, R. Santana, and J. A. Lozano, Department of Computer Science and Artificial Intelligence. University of the Basque Country technical report EHU-KAT-IK-11/07, Oct. 2007.

Abstract

This paper incorporates Belief Propagation into an instance of Estimation of Distribu- tion Algorithms called Estimation of Bayesian Networks Algorithm. Estimation of Bayesian Networks Algorithm learns a Bayesian network at each step. The objective of the proposed variation is to increase the search capabilities by extracting information of the, computa- tionally costly to learn, Bayesian network. Belief Propagation applied to graphs with cycles, allows to ÃÂ&hibar;nd (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 modi- ÃÂ&hibar;cation can increase the potentialities of Estimation of Distribution Algorithms. Due to the computational time implied in the resolution of high dimensional optimization problems, we give a parallel version of the Belief Propagation algorithm for graphs with cycles and intro- duce it in a parallel framework for Estimation of Distribution Algorithms [13]. In addition we point out many ideas on how to incorporate Belief Propagation algorithms into Estimation Distribution Algorithms.

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BibTeX entry:

@techreport{Mendiburu_et_al:2007a,
   author = {A. Mendiburu and R. Santana and J. A. Lozano},
   title = {Introducing belief propagation in estimation of distribution
	algorithms: A parallel framework},
   institution = {Department of Computer Science and Artificial
	Intelligence, University of the Basque Country},
   number = {EHU-KAT-IK-11/07},
   month = oct,
   year = {2007}
}

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