Volume 9 Number 12 (Dec. 2014)
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JCP 2014 Vol.9(12): 2787-2791 ISSN: 1796-203X
doi: 10.4304/jcp.9.12.2787-2791

A Hybrid Optimization Algorithm for Bayesian Network Structure Learning Based on Database

Junyi Li 1 and Jingyu Chen2
1Department of Computer Engineering, Dongguan Polytechnic, Dongguan, China
2School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China


Abstract—The process of learning Bayesian networks includes structure learning and parameters learning. During the process, learning the structure of Bayesian networks based on a large database is a NP hard problem. The paper presents a new hybrid algorithm by integrating the algorithms of MMPC (max-min parents and children), PSO (particle swarm optimization) and GA (genetic algorithm) effectively. In the new algorithm, the framework of the undirected network is firstly constructed by MMPC, and then PSO and GA are applied in score-search. With the strong global optimization of PSO and the favorable parallel computing capability of GA, the search space is repaired efficiently and the direction of edges in the network is determined. The proposed algorithm is compared with conventional PSO and GA algorithms. Experimental results show that the proposed algorithm is most effective in terms of convergence speed.

Index Terms—Bayesian network, particle swarm optimization, genetic algorithm, crossover, mutation.

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Cite: Junyi Li and Jingyu Chen, "A Hybrid Optimization Algorithm for Bayesian Network Structure Learning Based on Database," Journal of Computers vol. 9, no. 12, pp. 2787-2791, 2014.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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