DBN Structure Adaptive Learning Algorithm Based on Improved Genetic Algorithms
SUN Mei-ting1,2,LIU Bin2
1. School of artificial intelligence and automation, Ministry of Information Science, Beijing University of Technology, Beijing 100124, China
2. School of College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract Aiming at the NP-hardness problem of dynamic bayesian network, dynamic bayesian structure adaptive learning algorithm based on improved Genetic Algorithm is proposed. The maximum mutual information and timing mutual information are first used in the proposed algorithm to build initial structure, completing the initialization of the search space for DBN structures. Based on this, an improved genetic algorithm is presented. An adaptive control function of crossover probability and mutation probability is constructed introducing the grading standard deviation in order to reduce the probability of getting trapped in a local optimum. Compared with other optimization algorithm, experimental results indicates that the IMGA-DBN algorithm can significantly decrease nearly 30% and 37% in the hamming distance and running time separately. Meanwhile, IMGA-DBN increase 18.0% in the scoreing metric values without prior information.
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2021, Vol. 42 
