火力与指挥控制2026，Vol.51Issue(4)：50-60,11.DOI:10.3969/j.issn.1002-0640.2026.04.007
基于改进遗传算法的贝叶斯网络结构学习
Bayesian Network Structure Learning Based on Improved Genetic Algorithm
摘要
Abstract
The scale of the Bayesian network structure search space increases exponentially with the number of nodes,which makes BN structure learning extremely difficult.Meanwhile,traditional genetic algorithm for BN structure learning easily falls into local optima.This paper adopts an improved genetic algorithm for Bayesian network structure learning.First,the population is initialized using conditional independence tests and mutual information.Then,the population is updated and iterated with the tournament selection strategy,improved crossover operator,and mutation operator in the search space generated by conditional independence tests.Finally,the optimal individual,i.e.,the final network structure,is selected according to the fitness function.Experimental results show that the proposed algorithm achieves higher accuracy and faster convergence speed in BN structure learning.关键词
贝叶斯网络/结构学习/条件独立性检验/互信息/遗传算法Key words
Bayesian network/structure learning/conditional independence test/mutual informa-tion/genetic algorithm分类
信息技术与安全科学引用本文复制引用
原森浩,李海霞,王承智,王克江,安健鹏..基于改进遗传算法的贝叶斯网络结构学习[J].火力与指挥控制,2026,51(4):50-60,11.基金项目
国家自然科学基金联合基金资助项目(U24A20263) （U24A20263）
