Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

被引：0

作者：

Orozco-Monteagudo, Maykel ^{[1
]}

Taboada-Crispi, Alberto ^{[1
]}

Gutierrez-Hernandez, Liliana ^{[1
]}

机构：

[1] Univ Cent Las Villas, Ctr Studies Elect & Informat Technol, Santa Clara 54830, Villa Clara, Cuba

来源：

INTERNATIONAL ELECTRONIC CONFERENCE ON COMPUTER SCIENCE | 2008年 / 1060卷

关键词：

Neural networks; genetic algorithms; crossover probability selection; mutation probability selection; replacement rate selection;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

引用

页码：3 / 6

页数：4

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