CAJ | 학술논문

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.
기우무약속우화화유전산법,제출일충학습패협사망락결구적한제형유전산법.수선구조일무약속우화문제,기최우해대응일개무향도.재무향도적기출상,산생유전산법적초시충군,병사용유전산법중적선택、교차화변이산자학습득도최우패협사망락결구.유우산생초시충군적공간시유일사최우패협사망락결구적후선변구성,초시충군구유흔호적성질.여직접사용유전산법학습패협사망락결구적효솔상비,해방법적학습효솔상대교고.
@@@@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.