Use of genetic algorithms with back propagation in training of feed-forward neural networks

Michael McInerney, Atam P. Dhawan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Scopus citations

Abstract

The problem of genetic algorithms is that they are inherently slow. In this paper we present a hybrid of genetic and back-propagation algorithms (GA-BP) which should always find the correct global minima without getting stuck at local minima. Various versions of the GA-BP method are presented and experimental results show that GA-BP algorithms are as fast as the back-propagation algorithm and do not get stuck at local minima. The proposed GA-BP algorithms are also not sensitive to the values of momentum and learning rate used in back-propagation and can be made independent of the learning rate and momentum. It is shown that the adaptive GA-BP algorithm can provide the optimal learning rate and better performance than simple back-propagation.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages203-208
Number of pages6
ISBN (Print)0780312007
StatePublished - Jan 1 1993
Externally publishedYes
Event1993 IEEE International Conference on Neural Networks - San Francisco, CA, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Neural Networks

Other

Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, CA, USA
Period3/28/934/1/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

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