Bayesian Network Structure Learning Based on Improved Hybrid Genetic Bacterial Foraging Algorithm
LIU Hao-ran1,2,CHANG Jin-feng1,PANG Na-na1,LI Chen-ran1,LU Ze-dan1
1. Information Science and Engineering College of Yanshan University, Qinhuangdao, Hebei 066004, China
2. Hebei Province Key Laboratory of Special Optical Fiber & Optical Fiber Sensing, Qinhuangdao,Hebei 066004, China
Abstract：Aiming at the problem that learning Bayesian network by heuristic learning algorithm is easy to get into local optimization and low searching efficiency, a hybrid improved genetic bacteria foraging optimization algorithm for learning Bayesian network structure was proposed. Firstly, the optimal population was obtained by genetic algorithm and used as the initial population of bacteria foraging algorithm. Then, in order to increase population diversity and expand search space, crossover and mutation strategies were used to improve the replication behavior of bacteria foraging algorithm. Finally, the population was updated through the initialization operation of the migration behavior of the improved bacteria foraging algorithm to prevent the loss of elite individuals. Through the iterative search of the population, the optimal Bayesian network structure was finally obtained.The simulation results show that the convergence accuracy and efficiency of this algorithm are improved compared with other algorithms.
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2020, Vol. 41 
