Bayesian network (BN) is a useful tool to represent joint probability distribution in the form of graphical model providing flexible inference and uncertainty handling. If there is enough knowledge about domain, it is possible to design the structure and parameters of BN by expert. Also, it can be learned from massive dataset with statistical learning algorithm. Usually, because the search space of Bayesian networks is relatively huge compared to the other models, evolutionary algorithms have been used to find optimal structure and parameters by many researchers. In this paper, we have focused on the topic of adaptation of constructed models for better performance. If there are a number of models constructed or learned by different experts or sources, it is better to fuse them into one model by considering all the information of each model. However, the complexity of the integrated model is relatively higher than previous isolated models. Minimizing the complexity of the integrated model using evolutionary algorithm is proposed. After integrating models into single one, it needs to adapt to the new data from the environment. It is likely to provide wrong results to the newly generated data from the environment and slightly modifying the joint probability distribution is necessary. The refinement process is also guided by the evolutionary algorithm because the space of search is large. Experimental results on a benchmark network show that the proposed adaptation methods with evolutionary algorithm can perform better than heuristics or greedy approaches.