A Hybrid Genetic Algorithm with Fitness Sharing Based on Rough Sets Theory

Author

Feng, Jihua ; Li, Wenjuan ; Shi, Xinling ; Chen, Jianhua

Author_Institution

Dept. of Electron. Eng., Yunnan Univ., Kunming

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3340

Lastpage

3344

Abstract

This paper presents a new method integrated sharing genetic algorithm (SGA), rough sets theory (RST) and bit-climbing algorithm for multimodal function optimization. We apply the SGA to complete the globe search and form niches which indicate the promising locations. Then, we utilize the strong qualitative analysis ability of rough sets to identify these niches. Finally, the bit-climbing algorithm is used to complete the local search and refine the solution in each of niches. The experiment has proved that using this approach to solve the multimodal function optimization is efficient both in robustness and in accuracy

Keywords

genetic algorithms; rough set theory; bit-climbing algorithm; fitness sharing; hybrid genetic algorithm; multimodal function optimization; rough sets theory; sharing genetic algorithm; Genetic algorithms; Genetic engineering; Information analysis; Information systems; Optimization methods; Pattern analysis; Robustness; Rough sets; Standards development; Uncertainty; Rough sets; Sharing genetic algorithm; bit-climbing algorithm; niche count;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712986

Filename

1712986