Bayesian network structure learning based on hybrid genetic and fish swarm algorithm

被引：1

作者：

Guo, Tong ^{[1
]}

Lin, Feng ^{[1
]}

机构：

[1] School of Electrical Engineering, Zhejiang University

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2014年 / 48卷 / 01期

关键词：

Artificial fish swarm algorithm; Bayesian network; Cloud-based adaptive theory; Genetic algorithm;

D O I：

10.3785/j.issn.1008-973X.2014.01.020

中图分类号：

学科分类号：

摘要：

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.

引用

页码：130 / 135

页数：5

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