Please wait a minute...
J4  2014, Vol. 48 Issue (1): 130-135    DOI: 10.3785/j.issn.1008-973X.2014.01.020
电气工程     
基于混合遗传鱼群算法的贝叶斯网络结构学习
郭童,林峰
浙江大学 电气工程学院,浙江 杭州 310027
Bayesian network structure learning based on hybrid genetic
and fish swarm algorithm
GUO Tong,LIN Feng
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China 
 全文: PDF(746 KB)  
摘要:

提出基于云自适应的遗传鱼群算法的结构学习方法.该方法利用最大生成树准则得到初始种群,将遗传算法中的交叉、变异思想分别应用于人工鱼群算法的聚群、追尾、觅食阶段,从而改进鱼群算法进行初始种群的优化.由于鱼群算法的觅食阶段的较强随机性,将云自适应理论应用于觅食阶段生成变异率.在ASIA和ALARM上的仿真实验证明,混合遗传鱼群算法在贝叶斯网络结构学习中具有很强的寻优能力.

关键词: 贝叶斯网络人工鱼群算法遗传算法云自适应理论    
Abstract:

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.

Key words: Bayesian network    artificial fish swarm algorithm    genetic algorithm    cloud-based adaptive theory
出版日期: 2014-02-21
:  TP 393  
通讯作者: 林峰,男,副教授.     E-mail: eeflin@zju.edu.cn
作者简介: 郭童(1988-),男,硕士生,从事智能信息处理和知识发现的研究.E-mail:773900604@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

郭童,林峰. 基于混合遗传鱼群算法的贝叶斯网络结构学习[J]. J4, 2014, 48(1): 130-135.

GUO Tong,LIN Feng. Bayesian network structure learning based on hybrid genetic
and fish swarm algorithm. J4, 2014, 48(1): 130-135.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2014.01.020        http://www.zjujournals.com/xueshu/eng/CN/Y2014/V48/I1/130

[1] HECKMAN D,WELLMAN M. Bayesian networks [J]. CACM,1995,38(3):27-30.
[2] FRIDEMAN N,LINIAL M,NACHMAN I. Using Bayesian networks to analyze data [J]. Journal of Computational Biology,2007(3):601-620.
[3] CHICKERING D M,HECKERMAN D,MEEK C. Large-sample learning of Bayesian networks is NP-hard [J]. Journal of Machine Learning Research,2004(5):1287-1330.
[4] CHENG J, BELL D A, LIU W. An algorithm for Bayesian belief network construction from data [C]∥Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics. Lauderdale,Florida:[s. n.],1997:83-90 .
[5] CHOW C,LIU C. Approximation discrete probability distributions with dependence trees [J]. IEEE Transactions on Information Theory,1968,14(3):462-467.
[6] LARRANAGA P,POZA M,YURRAMENDI Y,et al. Structure learning of Bayesian networks by genetic algorithms:a performance analysis of control parameters [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(9):912-926.
[7] 李晓磊,邵之江,钱积新. 一种基于动物自治体的寻优模式:鱼群算法[J] . 系统工程理论与实践, 2002(11):32-38.
LI Xiao-lei,SHAO Zhi-jiang,QIAN Ji-xin.An optimizing method based on autonomous animats:fish swarm algorithm [J]. Systems Engineering: Theory and Practice,2002(11):32-38.
[8] 李德毅,孟海军,史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展,1995,32(6):15-20 .
LI De-yi,MENG Hai-jun,SHI Xue-mei. Membership clouds and membership cloud generators [J]. Journal of Computer Research and Development,1995,32(6):15-20.
[9] 许丽佳,黄建国,王厚军,等. 混合优化的贝叶斯网络结构学习[J]. 计算机辅助设计与图形学学报, 2009,21(5):633-639.
XU Li-jia,HUANG Jian-guo,WANG Hou-jun, et al. Hybrid optimized algorithm for learning Bayesian network structure [J]. Journal of Computer-aided design and Computer Graphics,2009, 21(5):633-639.
[10] 沈佳杰,林峰.基于混合自适应Memetic算法的贝叶斯网络结构学习[J].系统工程与电子技术,2012, 34(6): 1293-1298.
SHEN Jia-jie,LIN Feng. Structure learning of Bayesian network using adaptive hybrid memetic algorithm [J]. Systems Engineering and Electronics,2012,34(6):1293-1298.
[11] 戴朝华,朱云芳,陈维荣.云自适应遗传算法[J] .控制理论与应用, 2007,24(4):646-650.
DAI Chao-hua,ZHU Yun-fang,CHEN Wei-rong. Adaptive genetic algorithm based on cloud theory [J]. Control Theory and Applications,2007,24(4):646650.
[12] 王翔,郑建国,张超群,等.采用约束蚁群优化的贝叶斯网结构学习算法[J].西安交通大学学报,2011,45(8):54-61.
WANG Xiang,ZHENG Jian-guo,ZHANG Chao-qun, et al. A constrained ant colony optimization algorithm for learning Bayesian networks [J]. Journal of Xi’an Jiaotong University,2011,45(8):54-61.
[13] 邸若海,高晓光.基于限制型粒子群优化的贝叶斯网络结构学习[J].系统工程与电子技术,2011,33(11):2423-2427.
DI Ruo-hai, GAO Xiao-guang. Bayesian network structure learning based on restricted particle swarm optimization [J]. Systems Engineering and Electronics,2011,33(11):2423-2427.
[14] CHICKERING D M. Optimal structure identification with greedy search [J]. Journal of Machine Learning Research,2002,3:507-554.

[1] 赵斌, 张松, 李剑峰. 基于零件摩擦学性能的磨削参数优化[J]. 浙江大学学报(工学版), 2018, 52(1): 16-23.
[2] 张玄武, 郑耀, 杨波威, 张继发. 基于级联前向网络的翼型优化设计[J]. 浙江大学学报(工学版), 2017, 51(7): 1405-1411.
[3] 张丽娜, 余阳. 海量O2O服务组合的优化[J]. 浙江大学学报(工学版), 2017, 51(6): 1259-1268.
[4] 苏亮, 宋明亮, 董石麟, 罗尧治. 循环遗传聚类法稳定图自动分析[J]. 浙江大学学报(工学版), 2017, 51(3): 514-523.
[5] 徐哲, 熊晓锋, 洪嘉鸣, 何必仕, 陈云. 数据驱动的城市供水管网异常事件侦测方法[J]. 浙江大学学报(工学版), 2017, 51(11): 2222-2231.
[6] 张俊红,郭迁,王健,徐喆轩,陈孔武. 塑料机油冷却器盖加强筋参数的多目标优化[J]. 浙江大学学报(工学版), 2016, 50(7): 1360-1366.
[7] 司恩波, 王晶, 靳其兵, 周靖林. 工业无线网络链路选择与时隙分配的同步优化[J]. 浙江大学学报(工学版), 2016, 50(6): 1203-1213.
[8] 王树朋,黄凯,严晓浪. 基于遗传算法的覆盖率驱动测试产生器[J]. 浙江大学学报(工学版), 2016, 50(3): 580-588.
[9] 李清,胡志华. 基于多目标遗传算法的灾后可靠路径选择[J]. 浙江大学学报(工学版), 2016, 50(1): 33-40.
[10] 刘扬,鲁乃唯,蒋友宝. 结构体系可靠度分析的改进支持向量回归[J]. 浙江大学学报(工学版), 2015, 49(9): 1692-1699.
[11] 高史义, 罗小华, 卢宇峰, 刘富春, 张晨秋. 基于遗传算法的功能覆盖率收敛技术[J]. 浙江大学学报(工学版), 2015, 49(8): 1509-1515.
[12] 苗峰,谢安桓,王富安,喻峰,周华. 多阶段可替换分组并行机调度问题的求解[J]. 浙江大学学报(工学版), 2015, 49(5): 866-872.
[13] 赵琼,童水光,钟崴,葛俊旭. 基于GA-FEA的门座起重机变幅机构优化设计[J]. 浙江大学学报(工学版), 2015, 49(5): 880-886.
[14] 艾小祥,俞慈君,方强,陈磊,方伟,沈立恒. 基于遗传算法的机翼壁板扫描路径优化[J]. 浙江大学学报(工学版), 2015, 49(3): 448-456.
[15] 过海,倪益华,王进,陆国栋. 车用空调冷凝器性能多目标优化方法[J]. 浙江大学学报(工学版), 2015, 49(1): 142-159.