基于遗传算法的因果图网络结构学习

Learning Causality Diagram Structure Based on Genetic Algorithm

在因果图理论中,采用了图形化和直接因果强度来表达知识和因果关系,它克服了贝叶斯网的一些不足,已发展成了一个能够处理离散变量和连续变量的混合模型.但是因果图的结构得由领域专家给出,这在实际中很难办到.鉴于因果图结构的复杂度随论域中节点个数的增加呈指数上升,寻找最有可能因果图网络结构成为了NP-HARD难题.文中给出了如何利用已知数据集,寻找最有可能的因果图网络结构设计的遗传算法(Genetic A lgorithm,GA).

The Causality Diagram theory, which adopted graphical expression of knowledge and direct causality intensity of causality, overcomes some shortages in Belief Network and has evolved into a mixed causality diagram methodology coped with discrete and continuous variable. But it is difficult that the structure of Causality Diagram given by expert. Because the complexity of causality diagram structure goes up exponentially through the number of the vertex＇ s increasing, it is NP-hard problem to find the most possible structure from a set of data. The authors discuss approaches and present Genetic Algorithm, to find the most possible structure from a set of data. Experiment shows the method is effective.