Research on Bayesian network structure learning method based on hybrid mountain-climbing algorithm and genetic algorithm

2017 ◽  
pp. 587-590
Author(s):  
Wei Xu ◽  
Gang Cheng ◽  
Lin Huang
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Method ◽  
Mountain Climbing ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian Network Structure Learning Based on Unconstrained Optimization and Genetic Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1366-1369
Author(s):  
Xiu Jian Lv ◽  
Rui Tao Liu
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Unconstrained Optimization ◽  
Network Structure ◽  
Structure Learning ◽  
Undirected Graph ◽  
Initial Population ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Mutation Operators

Based on unconstrained optimization and genetic algorithm, this paper presents a constrained genetic algorithm (CGA) for learning Bayesian network structure. Firstly, an undirected graph is obtained by solving an unconstrained optimization problem. Then based on the undirected graph, the initial population is generated, and selection, crossover and mutation operators are used to learn Bayesian network structure. Since the space of generating the initial population is constituted by some candidate edges of the optimal Bayesian network, the initial population has good property. Compared with the methods which use genetic algorithm (GA) to learn Bayesian network structure directly, the proposed method is more efficiency.

scholarly journals Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Jianxiao Liu ◽  
Zonglin Tian
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Method ◽  
Dependency Analysis ◽  
Learning Methods ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Three Phase

Background and Objective. Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. Methods. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. Results. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. Conclusions. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

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