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Titulo: A Comparison Between ANN Generation and Training Methods and Their Development by Means of Graph Evolution: 2 Sample Problems
Tipo: revista internacional
Fecha: 6,2007
Revista: Lecture Notes in Computer Science. Computational and Ambient Intelligence
JCR Journal; Impact Factor: 0.402
Volumen: LNCS 4507
Paginas: 94-101
ISSN: 0302-9743
Editorial: Springer-Verlag
Heidelberg, Berlin
doi: 10.1007/978-3-540-73007-1_12


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.

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