Improved Bayesian optimization algorithm incorporated prior knowledge

Since it is difficult to obtain the prior knowledge of general optimization problems, many algorithms of learning Bayesian networks almost do not incorporate or use the prior knowledge of problems. And it is NP-hard to learn the Bayesian networks. According to the characteristics of the Bayesian optimization algorithm (BOA), it was discussed on how to discovery and use prior knowledge in general optimization problems. An improved Bayesian optimization algorithm incorporated prior knowledge was proposed. The information provided by the previous generation was considered as prior knowledge to be incorporated in the Bayesian networks learning. So the reliability of the networks and the performance of the proposed algorithm were improved. Simulation results show that the proposed algorithm achieves a stronger ability in searching the global optima than those of traditional BOA.