Title

Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization

Authors

Authors

G. Bebis; M. Georgiopoulos;T. Kasparis

Comments

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Abbreviated Journal Title

Neurocomputing

Keywords

neural networks; genetic algorithms; weight elimination; pruning; ARTIFICIAL NEURAL NETWORKS; Computer Science, Artificial Intelligence

Abstract

Recent theoretical results support that decreasing the number of free parameters in a neural network (i.e., weights) can improve generalization. These results have triggered the development of many approaches which try to determine an ''appropriate'' network size for a given problem. The main goal has been to find a network size just large enough to capture the general class properties of the data. In some cases, however, network size is not reduced significantly or the reduction is satisfactory but generalization is affected. In this paper, we propose the coupling of genetic algorithms with weight elimination. Our objective is not only to significantly reduce network size, by pruning larger size networks, but also to preserve generalization, that is, to come up with pruned networks which generalize as good or even better than their unpruned counterparts. The innovation of our work relies on a fitness function which uses an adaptive parameter to encourage reproduction of networks having small size and good generalization. The proposed approach has been tested using both artificial and real databases demonstrating good performance.

Journal Title

Neurocomputing

Volume

17

Issue/Number

3-4

Publication Date

1-1-1997

Document Type

Article

Language

English

First Page

167

Last Page

194

WOS Identifier

WOS:A1997YF09602324

ISSN

0925-2312

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