DocumentCode :
1538406
Title :
The optimal basics for GAs
Author :
Kamepalli, Harinath Babu
Author_Institution :
Sun Microsyst. Inc., Sunnyvale, CA, USA
Volume :
20
Issue :
2
fYear :
2001
Firstpage :
25
Lastpage :
27
Abstract :
Genetic algorithms were introduced by John Holland in early 1970s as a special technique for function optimization. They are quite different from other more conventional optimization methods that are mainly stochastic in nature. A typical GA will have three phases; i.e., initialization, evaluation and genetic operation. In each phase, various parameters of GA need to be selected based on the nature of the optimization problem. A genetic algorithm is also classified based on the various combinations of parameters and strategies employed. However, the designer is free to develop a hybrid genetic algorithm. The main goal is to deliver the most enhanced performance possible to the optimization problem
Keywords :
genetic algorithms; GA; evaluation; function optimization; genetic operation; hybrid genetic algorithm; initialization; optimization methods; optimization problem; performance; Biological cells; Genetic algorithms; Machine learning; Machine learning algorithms; Robustness; Stochastic processes; Testing;
fLanguage :
English
Journal_Title :
Potentials, IEEE
Publisher :
ieee
ISSN :
0278-6648
Type :
jour
DOI :
10.1109/45.954645
Filename :
954645
Link To Document :
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