免疫遗传算法学习贝叶斯网等价类

Learning Equivalence Classes of Bayesian Network with Immune Genetic Algorithm

针对遗传算法学习贝叶斯网存在的问题,提出一种基于骨架搜索的免疫遗传算法学习贝叶斯网等价类,该方法综合了基于约束和打分搜索的方法,可以在遗传过程中避免产生非法结构,并从骨架空间映射到等价类空间进行搜索.实验数据表明,免疫算子的使用可有效缩小搜索空间规模,加快收敛速度,提高执行效率.

To the question of drawbacks in learning Bayesian network with genetic algorithm, an immune genetic algorithm was proposed and used to learn the structure of Bayesian network, which combines the constraint based approach with score-search based approach. The algorithm can avoid generating illegal structures ; by means of the property of Markov equivalence, the immune operators maps the search space from skeleton space to Markov equivalent class space. The experiment data show that the search space was decreased, compared with those of the genetic algorithm search in direct acyclic graph space, the convergence speed and the efficiency were improved.