Estimation of distribution algorithms with affinity propagation methods

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“Estimation of distribution algorithms with affinity propagation methods” by Roberto Santana, P. Larrañaga, and J. A. Lozano, Department of Computer Science and Artificial Intelligence. University of the Basque Country technical report EHU-KZAA-IK-1/08, Jan. 2008.

Abstract

Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems.In this paper, we introduce the affinity propagation EDA which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and non-binary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.

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BibTeX entry:

@techreport{Santana_et_al:2008,
   author = {Roberto Santana and P. Larra{\~n}aga and J. A. Lozano},
   title = {Estimation of distribution algorithms with affinity
	propagation methods},
   institution = {Department of Computer Science and Artificial
	Intelligence, University of the Basque Country},
   number = {EHU-KZAA-IK-1/08},
   month = jan,
   year = {2008}
}

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