DocumentCode :
2558233
Title :
An improved species conserving genetic algorithm for multimodal optimization
Author :
Dingcai Shen ; Xia, Xuewen
Author_Institution :
Sch. of Comput. & Inf. Sci., Hubei Eng. Univ., Xiaogan, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
1156
Lastpage :
1160
Abstract :
A new method for finding multiple solutions of multimodal optimization problems is proposed in this paper. To avoid the necessity of specifying a niche radius, the proposed method adopts hybrid of species conservation and hill-valley detection mechanism. The proposed method is compared with classical Species Conservation Genetic Algorithm (SCGA) on a number of standard benchmark problems. The experimental results show that the new approach performs better in finding all optima with no additional parameters introduced.
Keywords :
genetic algorithms; SCGA; hill-valley detection mechanism; multimodal optimization; niche radius; species conservation genetic algorithm; Algorithm design and analysis; Educational institutions; Evolutionary computation; Genetic algorithms; Optimization; Radio frequency; Standards; Genetic Algorithm; Hill-valley Detecting; Multimodal Optimization; Species Conservation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
ISSN :
2157-9555
Print_ISBN :
978-1-4577-2130-4
Type :
conf
DOI :
10.1109/ICNC.2012.6234613
Filename :
6234613
Link To Document :
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