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2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS | Vol., Issue. | | Pages 7

Self-adaptive differential evolution algorithm for numerical optimization

Suganthan, PN Qin, AK

Abstract

In this paper, we propose a novel Self-adaptive Differential Evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified. During evolution, the suitable learning strategy and parameter settings are gradually self-adapted according to the learning experience. The performance of the SaDE is reported on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization.

Original Text (This is the original text for your reference.)

Self-adaptive differential evolution algorithm for numerical optimization

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Keywords

benchmark functions learning strategy selfadaptive differential evolution algorithm control real parameter optimization sade

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Suganthan, PN,Qin, AK ,.Self-adaptive differential evolution algorithm for numerical optimization. (),7.

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