【摘要】 利用免疫进化算法(IEA),借助遗传和接种疫苗操作将基于打分和基于约束的两类Bayesian网结构学习方法有机地结合在一起,提出一种新的Bayesian网结构学习方法.通过与基于遗传算法的Bayesian网结构学习方法EGA(Expectation&Genetic A lgorithm)的对比试验表明,所提出算法的收敛速度更快、学习得到网络的精度更高.更多还原

【Abstract】 A new algorithm was put forward for learning Bayesian’s network structure.This algorithm is based on an improved Genetic Algorithm: Immune Evolutionary Algorithm(IEA).The IEA combines the two kinds of methods in learning’s Bayesian network,the score based method and the constraint based method,by(genetic) operation and vaccination operation.The experimental results show that the convergent speed of IEA is more rapid,and the precision of IEA is higher than that of EGA(Expectation & Genetic Algorithm,a method learning Bayesian network based on GA).更多还原