Exact Bayesian network learning in estimation of distribution algorithms

Abstract

This paper introduces exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Second, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished.