Immune allied genetic algorithm for Bayesian network structure learning
Qin Song, Feng Lin, Wei Sun, KC Chang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.