Learning Bayesian Network structures using Multiple Offspring Sampling

Abstract

Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs). Previous works in the literature suggest that it is worth pursuing the use of evolutionary strategies for identifying a suitable VO, when learning a Bayesian Network structure from data. This paper proposes a hybrid adaptive algorithm named VOMOS (Variable Ordering Multiple Offspring Sampling) where the new individuals are created using a set of recombination operators (crossover and mutation operators). Experiments performed in datasets revealed that the VOMOS approach is promising and tends to generate consistent and representative BNs.