Abstract

Bayesian networks (BNs) are widely used as one of the most effective models in bioinformatics, artificial intelligence, text analysis, medical diagnosis, etc. Learning the structure of BNs from data can be viewed as an optimization problem and is proved that this problem is NP-hard. Therefore, heuristic methods can be used as powerful tools to find high-quality networks. In this paper, an interesting approach which is based on Breeding Swarm has been used to learn BNs. Breeding Swarm is a hybrid GA/PSO which enable us to benefits the strengths of particle swarm optimization with genetic algorithms. In order to assess the proposed method, several real-world and benchmark applications are used. Results show that our method is a clear improvement on genetic algorithm and particle swarm optimization.