Evolutionary optimization using graphical models

We have previously shown that a genetic algorithm can be approximated by an evolutionary algorithm using the product of univariate marginal distributi ons of selected points as search distribution. This algorithm (UMDA) succes sfully optimizes difficult multi-modal optimization problems. For correlate d fitness landscapes snore complex factorizations of the search distributio n have to be used. These factorizations are used by the Factorized Distribu tion Algorithm FDA. In this paper we extend FDA to an algorithm which compu tes a factorization from the data. The factorization can be represented by a Bayesian network. The Bayesian network is used to generate the search poi nts.