A new genetic approach to structure learning of Bayesian networks

In this paper, a new approach to structure learning of Bayesian networks (BNs) based on genetic algorithm is proposed. The proposed method explores the wider solution space than the previous method. In the previous method, while the ordering among the nodes of the BNs was fixed their conditional dependencies represented by the connectivity matrix was learned, whereas, in the proposed method, the ordering as well as the conditional dependency among the BN nodes is learned. To implement this method using the genetic algorithm, we represent an individual of the population as a pair of chromosomes: The first one represents the ordering among the BN nodes and the second one represents their conditional dependencies. To implement proposed method new crossover and mutation operations which are closed in the set of the admissible individuals are introduced. Finally, a computer simulation exploits the real-world data and demonstrates the performance of the method.