Estimation of Bayesian network algorithm with GA searching for better network structure

Abstract

Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.