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.
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2014, Vol. 27 
