Abstract Bayesian network structure learning is the core of Bayesian network theory and the current algorithms of learning Bayesian network structures are always inefficient. A method of learning Bayesian network structure based on hybrid differential evolution and bee colony algorithm is proposed. The maximum weight spanning tree is used to generate the candidate networks, and then the differential evolution algorithm is used to optimize the initial populations. In the process of using the differential evolution algorithm, the bee colony algorithm is introduced into variation stage and optimizing cross stage, and better candidates are selected by applying cloud-based adaptive theory to the choose stage. Simulation results on classic Bayesian network show that the proposed algorithm has a strong searching ability in Bayesian network structure learning.
[1] Frideman N, Linial M, Nachman I. Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 2000, 7(3/4): 601-620 [2] Chickering D M, Heckerman D, Meek C. Large-Sample Learning of Bayesian Networks Is NP-Hard. Journal of Machine Learning Research, 2004, 5: 1287-1330 [3] Cheng J, Bell D, Liu W R, et al. Learning Bayesian Networks from Data: An Efficient-Theory Based Approach. Artificial Intelligence, 2002, 137(1/2): 43-90 [4] Storn R, Price K. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 1997, 11(4): 341-359 [5] Gao W F, Liu S Y, Jiang F, et al. Hybrid Artificial Bee Colony Algorithm. Systems Engineering and Electronics, 2011, 33(5): 1167-1170 (in Chinese) (高卫峰,刘三阳,姜 飞,等.混合人工蜂群算法.系统工程与电子技术, 2011, 33(5): 1167-1170) [6] Larranaga P, Poza M, Yurramendi Y, et al. Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(9): 912-926 [7] Sahin F, Yavuz M C, Arnavu Z, et al. Fault Diagnosis for Airplane Engines Using Bayesian Networks and Distributed Particle Swarm Optimization. Parallel Computing, 2007, 33(2): 124-143 [8] Bao L, Zeng J C. A Bi-group Differential Artificial Bee Colony Algorithm. Control Theory & Applications, 2011, 28(2): 266-272 (in Chinese) (暴 励,曾建潮.一种双种群差分蜂群算法.控制理论与应用, 2011, 28(2): 266-272) [9] Lu Y L, Zhou J Z, Li Y H, et al. Adaptive Differential Evolution Algorithm Combined with Chaotic Search. Computer Engineering and Applications, 2008, 44(10): 31-33 (in Chinese) (卢有麟,周建中,李英海,等.基于混沌搜索的自适应差分进化算法.计算机工程与应用, 2008, 44(10): 31-33) [10] Wei X Q, Zhou Y Q, Huang H J, et al. Adaptive Particle Swarm Optimization Algorithm Based on Cloud Theory. Computer Engineering and Applications, 2009, 45(1): 48-51 (in Chinese) (韦杏琼,周永权,黄华娟,等.云自适应粒子群算法.计算机工程与应用, 2009, 45(1): 48-51) [11] Shen J J, Lin F. Structure Learning of Bayesian Network Using Adaptive Hybrid Memetic Algorithm. Systems Engineering and Electronics, 2012, 34(6): 1293-1298 (in Chinese) (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.系统工程与电子技术, 2012, 34(6): 1293-1298) [12] Xu L J, Huang J G, Wang H J, et al. Hybrid Optimized Algorithm for Learning Bayesian Network Structure. Journal of Computer-Aided Design & Computer Graphics, 2009, 21(5): 633-639 (in Chinese) (许丽佳,黄建国,王厚军,等.混合优化的贝叶斯网络结构学习.计算机辅助设计与图形学学报, 2009, 21(5): 633-639) [13] Wu H, Wang W P, Yang F. Structure Learning Method of Bayesian Network with Prior Information. Systems Engineering and Electronics, 2012, 34(12): 2585-2591 (in Chinese) (吴 红,王维平,杨 峰.融合先验信息的贝叶斯网络结构学习方法.系统工程与电子技术, 2012, 34(12): 2585-2591) [14] Tsamardinos I, Brown L E, Aliferis C F. The Max-Min Hill-Clim-bing Bayesian Network Structure Learning Algorithm. Machine Learning, 2006, 65(1): 31-78
2014, Vol. 27 
