Variable transformations in estimation of distribution algorithms

Authors:
Davide Cucci;Luigi Malagò;Matteo Matteucci
Affiliations:
Department of Electronics and Information, Politecnico di Milano, Milano, Italy;Department of Electronics and Information, Politecnico di Milano, Milano, Italy;Department of Electronics and Information, Politecnico di Milano, Milano, Italy
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

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Abstract

In this paper we address model selection in Estimation of Distribution Algorithms (EDAs) based on variables trasformations. Instead of the classic approach based on the choice of a statistical model able to represent the interactions among the variables in the problem, we propose to learn a transformation of the variables before the estimation of the parameters of a fixed model in the transformed space. The choice of a proper transformation corresponds to the identification of a model for the selected sample able to implicitly capture higher-order correlations. We apply this paradigm to EDAs and present the novel Function Composition Algorithms (FCAs), based on composition of transformation functions, namely I-FCA and Chain-FCA, which make use of fixed low-dimensional models in the transformed space, yet being able to recover higher-order interactions.