期刊
SOFT COMPUTING
卷 11, 期 7, 页码 617-629出版社
SPRINGER
DOI: 10.1007/s00500-006-0124-0
关键词
differential evolution; control parameter; fitness function; optimization; self-adaptation
Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, which use different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.
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