[1]张平,刘三阳,朱明敏.基于人工蜂群算法的贝叶斯网络结构学习[J].智能系统学报,2014,9(3):325-329.[doi:10.3969/j.issn.1673-4785.201310014]
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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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]
基于人工蜂群算法的贝叶斯网络结构学习
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第3期
页码:
325-329
栏目:
学术论文—智能系统
出版日期:
2014-06-25
- Title:
- Structure learning of Bayesian networks by use of the artificial bee colony algorithm
- 分类号:
- TP181
- 摘要:
- 从数据集中学习贝叶斯网络结构是一个NP难问题。针对此问题提出基于遗传算子的人工蜂群算法。首先, 将贝叶斯网络结构映射为一种二进制编码; 其次, 根据贝叶斯网络的结构特点, 设计了蜜源的更新策略, 从而将学习贝叶斯网络结构的过程转化为蜂群寻找最优蜜源的过程。实验结果表明, 该算法应用于贝叶斯网络结构学习中的有效性。
- 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.
- 参考文献/References:
-
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备注/Memo
收稿日期:2013-11-04。
基金项目:国家自然科学基金资助项目(61075055);西安电子科技大学基本科研业务基金资助项目(K5051270013).
作者简介:刘三阳,男,1959年生,教授,博士生导师,主要研究方向为优化理论及其应用、网络算法。主持多项国家级项目,发表多篇学术论文;朱明敏,女,1985年生,讲师,博士后,主要研究方向为优化算法及其在贝叶斯网络结构学习中的应用。
通讯作者:张平,女,1988年生,硕士研究生,主要研究方向为优化算法、贝叶斯网络结构学习,E-mail:pzhangxdedu@163.com。
更新日期/Last Update:
1900-01-01