Genetic optimization of self-organizing feature maps

Steven Alex Harp, Tariq Samad

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

17 Scopus citations


The authors present an application of the genetic algorithm to the design of Kohonen self-organizing feature maps. The genetic algorithm is used to optimize various parameters of the network model for a given problem. Performance criteria relevant to clustering or vector quantization applications are considered: root mean square (RMS) error and an information-theoretic map entropy measure. Experimental results demonstrate the effectiveness of the approach, and suggest some interesting generalizations.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN-91-Seattle
Subtitle of host publicationInternational Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0780301641
StatePublished - Dec 1 1991
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA


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