Learning dynamic Bayesian network with immune evolutionary algorithm
Haiyang Jia, Dayou Liu, Peng Yu · 2005
Dynamic Bayesian networks (DBNs) are directed graphical models of stochastic processes. How to learn the structure of DBNs from data is a hot problem of research. In this paper, the author presents an immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms.