Training of multilayer perceptron neural networks by using cellular genetic algorithms

Authors:
M. Orozco-Monteagudo;A. Taboada-Crispí;A. Del Toro-Almenares
Affiliations:
Center for Studies on Electronics and Information Technologies, Universidad Central de Las Villas, Santa Clara, VC, Cuba;Center for Studies on Electronics and Information Technologies, Universidad Central de Las Villas, Santa Clara, VC, Cuba;Center for Studies on Electronics and Information Technologies, Universidad Central de Las Villas, Santa Clara, VC, Cuba
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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Abstract

This paper deals with a method for training neural networks by using cellular genetic algorithms (CGA). This method was implemented as software, CGANN-Trainer, which was used to generate binary classifiers for recognition of patterns associated with breast cancer images in a multi-objective optimization problem. The results reached by the CGA with the Wisconsin Breast Cancer Database, and the Wisconsin Diagnostic Breast Cancer Database, were compared with some other methods previously reported using the same databases, proving to be an interesting alternative.