Dynamic Bayesian networks are a representation for complex stochastic processes.How to learn structure of Dynamic Bayesian networks from data is a hot problem of research.An evolutionary algorithm is proposed.Fitness function based on expectation is presented to convert incomplete data to complete data utilizing current best dynamic Bayesian network of evolutionary process.Thus dynamic Bayesian networks can be learned by using two Bayesian networks

prior network and transition network

to reduce the computational complexity.Encoding is given

and genetic operators are designed which provides guarantee of convergence.Experimental results not only show this algorithm can be effectively used to learn Dynamic Bayesian networks structure from incomplete data sequences

but also illustrate the role of hidden variables and the influence of genetic control parameters on learned model.