DocumentCode
276590
Title
Genetic optimization of self-organizing feature maps
Author
Harp, Steven Alez ; Samad, Tariq
Author_Institution
Honeywell SSDC, Minneapolis, MN, USA
Volume
i
fYear
1991
fDate
8-14 Jul 1991
Firstpage
341
Abstract
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 error and an information-theoretic map entropy measure. Experimental results demonstrate the effectiveness of the approach, and suggest some interesting generalizations
Keywords
genetic algorithms; learning systems; neural nets; self-organising storage; Kohonen self-organizing feature maps; clustering; genetic algorithm; genetic optimisation; information theory; map entropy measure; network model; parameter optimisation; root mean square error; vector quantization; Algorithm design and analysis; Backpropagation; Design optimization; Entropy; Genetic algorithms; Network synthesis; Neural networks; Optimization methods; Space exploration; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0164-1
Type
conf
DOI
10.1109/IJCNN.1991.155200
Filename
155200
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