Structure Learning of Bayesian Network Via Hybrid Fish Swarm Optimization Algorithm
Journal Title: 河南科技大学学报（自然科学版） - Year 2016, Vol 37, Issue 4
Abstract
In order to improve the accuracy of learning Bayesian network structure from the data set,a new method was proposed based on the hybrid Fish swarm optimization algorithm. Firstly,the initial undirected graph was generated by the mutual information and the maximum likelihood tree,which was the foundation of the initial population. Then the individual remembering capacity and communicating capacity of particle swarm optimization algorithm were introduced into the artificial fish swarm algorithm to avoid the blindness of searching. Finally,the algorithm referring to the mutation and crossover operator of adaptive genetic algorithm was used to improve the optimization process. Simulation experiment results show that the improved algorithm has better optimization ability.
Authors and Affiliations
Mengjie CHEN, Yuan WAN, Kefeng WU, Hengqing TONG
Keywords
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- EP ID EP473728
- DOI 10.15926/j.cnki.issn1672-6871.2016.04.009
- Views 49
- Downloads 0
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