Implicit Model Selection based on Variable Transformations in Estimation of Distribution

In this paper we address the problem of model selection in Estimation of Distribution Algorithms from a novel perspective. We perform an implicit model selection by transforming the variables and choosing a low dimensional model in the new variable space. We apply such paradigm in EDAs and we introduce a novel algorithm called I-FCA, which makes use of the independence model in the transformed space, yet being able to recover higher order interactions among the original variables. We evaluated the performance of the algorithm on well known benchmarks functions in a black-box context and compared with other popular EDAs.