Hybrid Optimized Algorithm for Learning Bayesian Network Structure

Learning structure from large databases is one of the difficulties of learning Bayesian Networks．To cope with this problem,a new hybrid algorithm is proposed．By integrating PSO （particle swarm optimization） and GA effectively,it owns not only simply and strong global optimization of PSO,but also favorable parallel computing capability of GA．Therefore,the learning accuracy and efficiency can be increased．Finally the proposed algorithm is compared with other algorithms in typical Bayesian networks such as Asia and Cancer,experimental results show that the proposed algorithm is effective．