基于无约束优化和遗传算法的贝叶斯网络结构学习方法

基于无约束优化和遗传算法,提出一种学习贝叶斯网络结构的限制型遗传算法.首先构造一无约束优化问题,其最优解对应一个无向图.在无向图的基础上,产生遗传算法的初始种群,并使用遗传算法中的选择、交叉和变异算子学习得到最优贝叶斯网络结构.由于产生初始种群的空间是由一些最优贝叶斯网络结构的候选边构成,初始种群具有很好的性质.与直接使用遗传算法学习贝叶斯网络结构的效率相比,该方法的学习效率相对较高.

Bayesian network structure learning based on unconstrained optimization and genetic algorithm

Based on unconstrained optimization and genetic algorithm, this paper presents a constrained genetic
algorithm(CGA) for learning Bayesian network structure. Firstly, an undirected graph is obtained by solving an unconstrained
optimization problem. Then based on the undirected graph, the initial population is generated, and selection, crossover and
mutation operators are used to learn Bayesian network structure. Since the space of generating the initial population is
constituted by some candidate edges of the optimal Bayesian network, the initial population has good property. Compared
with the methods which use genetic algorithm(GA) to learn Bayesian network structure directly, the proposed method is
more efficiency.