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A Genetic Algorithm as a Learning Method Based on Geometric Representations
Gregory A. Holifield and Annie S. Wu
School of Electrical Engineering and Computer Science
University of Central Florida
Orlando, FL 32816, USA
greg.holifield@us.army.mil
aswu@cs.ucf.edu
Abstract. A number of different methods combining the use of neural networks and genetic algorithms have been described [1]. This paper discusses an approach for training neural networks based on the geometric representation of the network. In doing so, the genetic algorithm becomes applicable as a common training method for a number of machine learning algorithms that can be similarly represented. The experiments described here were specifically derived to construct claim regions for Fuzzy ARTMAP Neural Networks [2,3]
LNCS 2724, p. 1588 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003