Bayesian Network Structure Learning Based on Unconstrained Optimization and Genetic Algorithm

Abstract

Based on unconstrained optimization and genetic algorithm, this paper presents a constrained genetic algorithm (CGA) for learning Bayesian network structure. Firstly, an undirected graph is obtained by solving an unconstrained optimization problem. Then based on the undirected graph, the initial population is generated, and selection, crossover and mutation operators are used to learn Bayesian network structure. Since the space of generating the initial population is constituted by some candidate edges of the optimal Bayesian network, the initial population has good property. Compared with the methods which use genetic algorithm (GA) to learn Bayesian network structure directly, the proposed method is more efficiency.