GENNET-Toolbox: An Evolving Genetic Algorithm for Neural Network Training

被引：0

作者：

Gomez-Garay, Vicente ^{[1
]}

Irigoyen, Eloy ^{[1
]}

Artaza, Fernando ^{[1
]}

机构：

[1] Univ Basque Country, ETSI, Automat Control & Syst Engn Dept, Bilbao, Spain

来源：

HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, PT 1 | 2010年 / 6076卷

关键词：

Artificial Neural Networks; Genetic Algorithms; Training; Evolutionary Mutation; Hybridization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetic Algorithms have been used from 1989 for both Neural Network training and design. Nevertheless, the use of a Genetic Algorithm for adjusting the Neural Network parameters can still be engaging. This work presents the study and validation of a different approach to this matter by introducing a Genetic Algorithm designed for Neural Network training. This algorithm features a mutation operator capable of working on three levels (network, neuron and layer) and with the mutation parameters encoded and evolving within each individual. We also explore the use of three types of hybridization: post-training, Lamarckian and Baldwinian. These proposes in combination with the algorithm, show for a fast and powerful tool for Neural Network training.

引用

页码：368 / 375

页数：8

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