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《Journal of Xi'an Jiaotong University》 2011-08
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A Constrained Ant Colony Optimization Algorithm for Learning Bayesian Networks

WANG Xiang,ZHENG Jianguo,ZHANG Chaoqun,YAN Ruixia(Glorious Sun School of Business and Management,Donghua University,Shanghai 200092,China)  
A novel constrained ant colony optimization algorithm is proposed for learning Bayesian networks.An add-edge-rule is designed in the proposed algorithm based on the locally consistent scoring criterion of BDEu metric.The add-edge-rule is embedded into the frame of ant colony optimization so that the heuristic information can be dynamically used to restrict the candidate evaluations during the search process and the running time of the new algorithm can be reduced.In addition,the correctness of the add-edge-rule is proved in theory and the parameter sensitivity of the constrained ant colony optimization is analyzed in experiments.Empirical tests show that without loss of results accuracy,the convergence speed of the proposed algorithm is at least 40% faster than that of the ant colony optimization algorithm for large-scale learning Bayesian networks.
【Fund】: 国家自然科学基金资助项目(70971020)
【CateGory Index】: TP183
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