Variable Transformations in Estimation of Distribution Algorithms

Davide Cucci, Luigi Malagò, and Matteo Matteucci

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.

Keywords: Function Composition Algorithm, Transformation of Variables, Minimization of Mutual Information, Chain Model

LNCS 7491, p. 428 ff.

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