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GA-RBF: A Self-Optimising RBF Network

Ben Burdsall, Christophe Giraud-Carrier, GA-RBF: A Self-Optimising RBF Network. Proceedings of the Third International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'97), pp. 348–351. April 1997. PDF, 145 Kbytes.

Abstract

The effects of a neural network's topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self-optimising approach, driven by an evolutionary strategy, is taken. The algorithm uses output information and a computationally efficient approximation of RBF networks to optimise the K-means clustering process by co-evolving the two determinant parameters of the network's layout: the number of centroids and the centroids' positions. Empirical results demonstrate promise.

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