DocumentCode
1396783
Title
Efficient genetic algorithms for solving hard constrained optimization problems
Author
Sareni, B. ; Krähenbühl, L. ; Nicolas, A.
Author_Institution
CEGELY, Ecully, France
Volume
36
Issue
4
fYear
2000
fDate
7/1/2000 12:00:00 AM
Firstpage
1027
Lastpage
1030
Abstract
This paper studies many Genetic Algorithm strategies to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to show advantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation method
Keywords
convergence; electrostatics; genetic algorithms; capacitor profile; charge simulation method; electric field; electrode shape optimization technique; genetic algorithms; genetic operators; hard constrained optimization problems; niching methods; premature convergence avoidance; Biological cells; Constraint optimization; Convergence; Electrodes; Gaussian noise; Genetic algorithms; Genetic mutations; Optimization methods; Protection; Shape;
fLanguage
English
Journal_Title
Magnetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9464
Type
jour
DOI
10.1109/20.877616
Filename
877616
Link To Document