A factorized distribution algorithm based on polytrees

The class of factorized distribution algorithms (FDA) uses factorizations of the joint distribution of the best points. At each stage of the evolution, PDA algorithms build a model from which new points are efficiently sampled. This paper explores the class of single connected factorizations: polytrees. Using this class, we gain in efficiency and simplicity in the procedures for learning the networks. The price we have to pay is a less expressive power. However, sometimes the representation power of polytrees is adequate for optimization purposes.