Research on Bayesian Network Adaptive Knowledge Construction and Inference Based on Genetic Algorithm

Bayesian network (BN) is the main model for solving probabilistic inference problem. The current research of BN just focuses on structure learning or parameter learning. And it still lacked algorithm which can implement the consistence of structure building, parameter learning and knowledge inference, so that knowledge construction and application are not associated and cannot be verified. This paper proposes a novel approach of Bayesian network adaptive knowledge construction and inference which is based on genetic algorithm. This algorithm, which designs a new BN learning encodement, crossover and mutation operators with adjusted strategies and the fitness function with inferential error feedback, implements BN building in all processes of structure building, parameter learning, knowledge inference and feedback revise with samples supported. Results show that the new approach not only can optimize the structure and parameters learning synchronously, but also can revise inferential error adaptively, and has more satisfied and accurate inference result.