A comparison between ANN generation and training methods and their development by means of graph evolution: 2 sample problems

Authors:
Daniel Rivero;Julián Dorado;Juan R. Rabuñal;Marcos Gestal
Affiliations:
Department of Information & Communications Technologies, A Coruñña, Spain;Department of Information & Communications Technologies, A Coruñña, Spain;Department of Information & Communications Technologies, A Coruñña, Spain;Department of Information & Communications Technologies, A Coruñña, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is compared with the traditional evolutionary techniques used for ANN development. The technique used here is based on network encoding on graphs and also their performance and evolution. For this comparison, 2 different real-world problems have been solved using various tools, and the results are presented here. According to them, the results obtained with this technique can beat those obtained with other ANN development tools.