DocumentCode :
2996258
Title :
The role of crossover in an immunity based genetic algorithm for multimodal function optimization
Author :
Huang, Chien-Feng
Author_Institution :
Modeling, Algorithms & Informatics Group, Los Alamos Nat. Lab., NM, USA
Volume :
4
fYear :
2003
fDate :
8-12 Dec. 2003
Firstpage :
2807
Abstract :
When genetic algorithms are employed in multimodal function optimization, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, we use an immune system model to explore the role of crossover in GAs with respect to these two issues. The experimental results reported here shed more light into how crossover affects the GA´s search power in the context of multimodal function optimization. We also show that an adaptive crossover strategy successfully achieves the two goals simultaneously. These results on the effects of crossover are a step toward a deeper understanding of how GAs work, and thus how to design more robust GAs for solving multimodal optimization problems.
Keywords :
genetic algorithms; learning (artificial intelligence); search problems; immunity-based genetic algorithm; multimodal function optimization; search space; subpopulation; Design optimization; Genetic algorithms; Immune system; Machine learning; Maintenance engineering; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN :
0-7803-7804-0
Type :
conf
DOI :
10.1109/CEC.2003.1299444
Filename :
1299444
Link To Document :
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