Discerning Functional Connections in the Pulsed Neural Networks with the Dynamic Bayesian Network Structure Search Method Based on a Genetic Algorithm

2019 ◽  
Vol 26 (11) ◽  
pp. 1243-1252
Author(s):  
Chaoxuan Dong ◽  
Xiao-Yan Chen ◽  
Chao-Yi Dong
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Dynamic Bayesian Network ◽  
Search Method ◽  
Functional Connections ◽  
Bayesian Network Structure ◽  
Structure Search ◽  
Pulsed Neural Networks

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2007 ◽  
pp. 42-53 ◽  
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

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