Evolutionary Algorithm Using Random Multi-point Crossover Operator for Learning Bayesian Network Structures

Abstract

Variable Ordering plays an important role when inducing Bayesian Networks. Previous works in the literature suggest that the use of genetic/evolutionary algorithms (EAs) for dealing with VO, when learning a Bayesian Network structure from data, is worth pursuing. This work proposes a new crossover operator, named Random Multi-point Crossover Operator (RMX), to be used with the Variable Ordering Evolutionary Algorithm (VOEA). Empirical results obtained by VOEA are compared to the ones achieved by VOGA (Variable Ordering Genetic Algorithm), and indicated improvement in the quality of VO and the induced BN structure.