On the Learning of Bayesian Network Graph Structures via Evolutionary Programming

Due to their intuitive interpretation, Bayesian Network models are often sought as useful descriptive and predictive models for the available data. Learning algorithms trying to ascertain the best BN model (graph structure) are therefore of the greatest interest. In this paper we examine a number of Evolutionary Programming algorithms for this network induction problem. Our algorithms build on recent advances in the field and are based on selection and various kinds of mutation operators (working at both the directed acyclic and essential graph level). We carefully measure the merit and computational toll of these EP variants in a couple of benchmark tasks. Some preliminary conclusions are outlined.