A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.