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浙江大学学报(工学版)
J4  2014, Vol. 48 Issue (1): 130-135    DOI: 10.3785/j.issn.1008-973X.2014.01.020
电气工程     
基于混合遗传鱼群算法的贝叶斯网络结构学习
郭童,林峰
浙江大学 电气工程学院,浙江 杭州 310027
Bayesian network structure learning based on hybrid genetic
and fish swarm algorithm
GUO Tong,LIN Feng
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China 
 全文: PDF(746 KB)   HTML
摘要：

提出基于云自适应的遗传鱼群算法的结构学习方法.该方法利用最大生成树准则得到初始种群,将遗传算法中的交叉、变异思想分别应用于人工鱼群算法的聚群、追尾、觅食阶段,从而改进鱼群算法进行初始种群的优化.由于鱼群算法的觅食阶段的较强随机性,将云自适应理论应用于觅食阶段生成变异率.在ASIA和ALARM上的仿真实验证明,混合遗传鱼群算法在贝叶斯网络结构学习中具有很强的寻优能力.
Abstract:

The method of learning Bayesian network structure was proposed based on hybrid genetic and fish swarm algorithm. The method used the maximum weight spanning tree to generate the candidate networks. Then the artificial fish swarm algorithm referring to the ideas of crossover and mutation methods of genetic algorithm was used to optimize the initial populations. Because of the randomness of the stage of the searching food in the artificial fish swarm algorithm, the cloud-based adaptive theory was brought into this stage to improve it. Simulation experiments on ASIA and ALARM demonstrate that the approach has quite good optimization ability in Bayesian network structure learning．
出版日期: 2014-01-01
:  TP 393  
通讯作者: 林峰，男，副教授.     E-mail: eeflin@zju.edu.cn
作者简介: 郭童（1988-），男，硕士生，从事智能信息处理和知识发现的研究.E-mail：773900604@qq.com
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引用本文:

郭童,林峰. 基于混合遗传鱼群算法的贝叶斯网络结构学习[J]. J4, 2014, 48(1): 130-135.

GUO Tong,LIN Feng. Bayesian network structure learning based on hybrid genetic
and fish swarm algorithm. J4, 2014, 48(1): 130-135.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.01.020    或    http://www.zjujournals.com/eng/CN/Y2014/V48/I1/130

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