Classification Techniques of Neural Networks Using Improved Genetic Algorithms

Authors:
Ming Chen;Zhengwei Yao
Affiliations:
-;-
Venue:
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
Year:
2008

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Abstract

Classification is an important problem in data mining. This paper focuses on a method of optimizing classifiers of neural network by Genetic Algorithm based on principle of gene reconfiguration, and implement classification by training the weight. The paper uses shift reverse logic crossover operation and the improved genetic algorithm The article using the typical method for optimizing BP neural network weight is BP algorithm, which has such disadvantages as slow practice speed and easy for running into local minimum. The article uses genetic algorithm based on gene reconfiguration to largely resolve the problem. Genetic algorithm optimizes neural network, mainly including neural network structure evolvement and neural network connection weight evolvement. The article firstly uses Simple Genetic Algorithm (SGA) for network structure evolvement and then adopts genetic algorithm based on gene reconfiguration for network weight practice. Experiment results show that Improved Genetic Algorithm (IGA) improve classifying veracity.