[1]ZHANG Ping,LIU Sanyang,ZHU Mingmin.Structure learning of Bayesian networks by use of the artificial bee colony algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(3):325-329.[doi:10.3969/j.issn.1673-4785.201310014]
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Structure learning of Bayesian networks by use of the artificial bee colony algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 3 Page number: 325-329 Column: 学术论文—智能系统 Public date: 2014-06-25
Title:
Structure learning of Bayesian networks by use of the artificial bee colony algorithm
Author(s):
ZHANG Ping;  LIU Sanyang;  ZHU Mingmin
School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
Keywords:
Bayesian networks; NP-hard; artificial bee colony; genetic operators; structure learning
CLC:
TP181
DOI:
10.3969/j.issn.1673-4785.201310014
Abstract:
The learning structure of Bayesian networks from a data set is an NP-hard problem. To deal with this problem, an artificial bee colony algorithm based on genetic operators is proposed in this paper. The structure of the Bayesian network is mapped to binary encoding, and the updated strategy of nectar is designed according to the characteristics of the Bayesian network structure. Thus the process of structure learning of the Bayesian network is transformed into the process of the bee colony finding the optimal nectar. The experimental results show that the algorithm is valid in the structure learning of Bayesian networks.
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