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．
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