The success of evolutionary algorithms, in particular Factor-ized Distribution Algorithms (FDA), for many pattern recognition tasks heavily depends on our ability to reduce the number of function evaluations. This paper introduces a method to reduce the population size overhead. We use low order marginals during the learning step and then compute the maximum entropy joint distributions for the cliques of the graph. The maximum entropy distribution is computed by an Iterative Proportional Fitting embedded in a junction tree message passing scheme to ensure consistency. We show for the class of single connected FDA that our method outperforms the commonly-used PLS sampling.
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