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Proceedings Paper

Fuzzified genetic algorithm with prefiltering for adaptive optimization
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Paper Abstract

Genetic algorithm performance has been improved by adaptively modifying genetic operators, and by filtering out recurring chromosomes from the fitness evaluation process. The enhanced genetic algorithm has been applied to neural network topology selection and function optimization. The performance of the algorithm was evaluated in multiple function and problem domains, where it showed superior convergence speed.

Paper Details

Date Published: 13 October 1997
PDF: 4 pages
Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); doi: 10.1117/12.284217
Show Author Affiliations
Jeongdal Kim, Physical Optics Corp. (United States)
Andrew A. Kostrzewski, Physical Optics Corp. (United States)
Dai Hyun Kim, Physical Optics Corp. (United States)
Judy Chen, Physical Optics Corp. (United States)
Anatoly A. Vasiliev, Physical Optics Corp. (United States)
Gajendra D. Savant, Physical Optics Corp. (United States)
Tomasz P. Jannson, Physical Optics Corp. (United States)


Published in SPIE Proceedings Vol. 3165:
Applications of Soft Computing
Bruno Bosacchi; James C. Bezdek; David B. Fogel, Editor(s)

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