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Learning Bayesian network structures by estimation of distribution algorithms: An experimental analysis

Learning the structure of a Bayesian network (BN)from a data set is NP-hard. In this paper, we discuss a novel heuristic based on estimation of distribution algorithms (EDA), a new paradigm for evolutionary computation that is used as a search engine in the BN structure learning problem. The purpose of this work is to study the parameter setting of the EDA and to fix a "good" set of parameters. For this purpose, the EDA-based procedure is applied on several benchmarks to recover the original structure from data. The quality of the learned structure is assessed using several performance indexes.