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Learning neural network weights using genetic algorithms-improving performance by search-space reduction

Srinivas, M and Patnaik, LM (1991) Learning neural network weights using genetic algorithms-improving performance by search-space reduction. In: 1991 IEEE International Joint Conference on Neural Networks (Cat. No.91CH3065-0), 18-21 Nov. 1991, Singapore, pp. 2331-2336.

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Abstract

The authors present a technique for reducing the search-space of the genetic algorithm (GA) to improve its performance in searching for the globally optimal set of connection-weights. They use the notion of equivalent solutions in the search space, and include in the reduced search-space only one solution, called the base solution, from each set of equivalent solutions. The iteration of the GA consists of an additional step where the solutions are mapped to the respective base solutions. Experiments were conducted to compare the performance of the GAs with and without search-space reduction. The experimental results are presented and discussed.

Item Type: Conference Paper
Additional Information: 1991 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
Keywords: genetic algorithms;learning systems;neural nets;search problems
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 21 Jan 2008
Last Modified: 19 Sep 2010 04:38
URI: http://eprints.iisc.ernet.in/id/eprint/11187

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