On the use of polytrees in evolutionary optimization

Bayesian networks are useful tools for the representation of non-linear interactions among variables. Recently, they have been combined with evolutionary methods to form a new class of optimization algorithms: the Factorized Distribution Algorithms (FDAs). FDAs have been proved to be significantly better than their genetic ancestors. They learn and sample distributions instead of using crossover and mutation operators. Most of the members of the FDAs that have been designed, learn general Bayesian networks. However, in this work we study a FDA that learns polytrees, which are single connected directed graphs.