The evolutionary learning method of Bayesian network structure based on expert knowledge

Bayesian Network, which has been widely used for its significant advantages in causal inference, has great potential in product design. However, it is difficult to learn a reasonable network structure facing the problem with small data during the design process. In order to solve this problem, this paper introduces expert knowledge and Genetic Algorithm based on matrix coding into the process of Bayesian Network structure learning. Integrating the expert knowledge and data is a decent way to make up for the problem of insufficient data, and Genetic Algorithm is used to improve traditional structure learning algorithms, so as to obtain a more suitable structure conforming to the knowledge and data characteristics. The solutions illustrate that the Genetic Algorithm has some advantages compared with traditional structure learning method, and the use of expert knowledge can improve the rationality of the learned structure.