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Genetic Search for Optimal Representations in Neural Networks

Munro, Paul (1991) Genetic Search for Optimal Representations in Neural Networks. Technical Report. School of Library and Information Science, University of Pittsburgh, Pittsburgh, PA.

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Abstract

An approach to learning in feed-forward neural networks is put forward that combines gradual synaptic modification at the output layer with genetic adaptation in the lower layer(s). In this "GA-delta" technique, the alleles are threshold units (a set of weights and a threshold); a chromosome is a collection of such units, and hence defines a mapping from the input layer to a hidden layer. Genetic operators are defined on these chromosome-mapping to facilitate search for a mapping that renders the task solvable by a single layer of weights. The performance of GA-delta is presented on several tasks, and the effects of the various operators is studied.

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Details

Item Type: Monograph (Technical Report)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Munro, Paulpwm@pitt.eduPWM
Monograph Type: Technical Report
Date: March 1991
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Publisher: School of Library and Information Science, University of Pittsburgh
Place of Publication: Pittsburgh, PA
Institution: University of Pittsburgh
Department: School of Library and Information Science
Schools and Programs: School of Information Sciences > Library and Information Science
Refereed: No
University of Pittsburgh Series: iSchool Research Report Series
Other ID: LIS039/IS91007
Date Deposited: 10 Apr 2013 20:24
Last Modified: 01 Nov 2017 14:02
URI: http://d-scholarship.pitt.edu/id/eprint/18256

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