摘要
Memetic算法是一种基于种群的全局搜索和基于个体的局部启发式搜索的结合体,具有较高的全局搜索能力,将其成功应用于贝叶斯网络的结构学习。该算法在基本的遗传算法操作算子中,引入粒子群算法的基本思想,同时利用混沌的遍历性和云自适应的快速收敛性,提出了一种云自适应的混沌变异搜索进行局部搜索,实现全局优化,跳出局部最优。实验证明该算法在贝叶斯网络结构学习中具有很好的效果。
Memetic algorithm is a combination of global search based on populations and local search based on individuals. It has a high global search ability and is used successfully in structure learning of Bayesian network. The principle of the particle swarm optimization algorithm is incorporated into the proposed basic genetic algorithm operating operator. By means of the ergodicity and randomizity of the chaos algorithm and a higher convergence speed of the cloud-based adaptive algorithm, a local search using a cloud-based chaotic mutation is proposed, which can avoid the local optimum and find out the best network structure. The experiment results reveal that this algorithm can be effectively used for BN structure learning.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2012年第6期1293-1298,共6页
Systems Engineering and Electronics
关键词
贝叶斯网络
结构学习
MEMETIC算法
粒子遗传算法
混沌
云自适应
Bayesian network （BN） structure learning Memetic algorithm （MA）
particle swarm genetic （PSO） algorithm chaos cloud-based adaptive
