Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm
Qin Song, Feng Lin, Wei Sun, KC Chang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
A new BN structure learning method using a cloud-based adaptive immune genetic algorithm (CAIGA) is proposed. Since the probabilities of crossover and mutation in CAIGA are adaptively varied depending on X-conditional cloud generator, it could improve the diversity of the structure population and avoid local optimum. This is due to the stochastic nature and stable tendency of the cloud model. Moreover, offspring structure population is simplified by using immune theory to reduce its computational complexity. The experiment results reveal that this method can be effectively used for BN structure learning.