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.
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Cucci, D., Malagò, L., Matteucci, M. (2012). Variable Transformations in Estimation of Distribution Algorithms. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_43
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DOI: https://doi.org/10.1007/978-3-642-32937-1_43
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