A Distance-Based Mutation Operator for learning Bayesian Network structures using Evolutionary Algorithms

Variable Orderings (VOs) have been used as a restriction in the process of Bayesian Networks (BNs) induction. The VO information can significantly reduce the search space and allow some algorithms to reach good results. Previous works reported in the literature suggest that the combination of Evolutionary Algorithms (EAs) and VOs is worth when learning a Bayesian Network structure from data. However, most works on this area do not explore specific characteristics of the domain, thus, they simply apply classic evolutionary operators. In addition, most works did not report good results when applied to big BNs. This paper proposes a new mutation operator, named Distance-Based Mutation Operator (DMO), to be used with the Variable Ordering Evolutionary Algorithm (VOEA). Experimental results obtained by VOEA are compared to ones achieved by VOGA (Variable Ordering Genetic Algorithm), and indicated improvement in the quality of the obtained VO and in the BN induced structure.