Title

Coupling Weight Elimination And Genetic Algorithms

Abstract

Network size plays an important role in the generalization performance of a network. A number of approaches which try to determine an 'appropriate' network size for a given problem have been developed during the last few years. Although it is usually demonstrated that such approaches are capable of finding small size networks that solve the problem at hand, it is quite remarkable that the generalization capabilities of these networks have not been thoroughly explored. In this paper, we have considered the weight elimination technique and we propose a scheme where it is coupled with genetic algorithms. Our objective is not only to find smaller size networks that solve the problem at hand, by pruning larger size networks, but also to improve generalization. The innovation of our work relies on a fitness function which uses an adaptive parameter to encourage the reproduction of networks having good generalization performance and a relatively small size.

Publication Date

1-1-1996

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Volume

2

Number of Pages

1115-1120

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0029727458 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0029727458

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