Structure learning of Bayesian networks using a semantic genetic algorithm-based approach

A Bayesian network model is a popular technique for data mining due to its intuitive interpretation. This paper presents a semantic genetic algorithm (SGA) to learn a complete qualitative structure of a Bayesian network from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. Particularly, we introduce two semantic crossover and mutation operators that aid in faster convergence of the SGA. The crossover and mutation operators in SGA incorporate the semantic of the Bayesian network structures to learn the structure with very minimal errors. SGA has been proved to perform better than existing classical genetic algorithms for learning Bayesian networks. We present empirical results to prove the fast convergence of SGA and the predictive power of the obtained Bayesian network structures.