Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7173
Title: Coupling weight elimination and genetic algorithms
Authors: Kasparis, Takis 
Bebis, George N.
Georgiopoulos, Michael N.
Keywords: Functions
Genetic algorithms
Neural networks
Computer networks
Issue Date: 1996
Publisher: IEEE
Source: IEEE International Conference on Neural Networks, 1996, Volume 2, Pages 1115-1120
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.
URI: http://ktisis.cut.ac.cy/handle/10488/7173
ISBN: 0-7803-3210-5
DOI: 10.1109/ICNN.1996.549054
Rights: © 1996 IEEE
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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