A self-organizing genetic algorithm for multimodal function optimization

A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification.
Publisher
Springer Verlag
Issue Date
1998-01
Language
ENG
Description

This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996

Citation

ARTIFICIAL LIFE AND ROBOTICS, v.2, no.1, pp.48 - 52

ISSN
1433-5298
URI
http://hdl.handle.net/10203/8346
Appears in Collection
EE-Journal Papers(저널논문)
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