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...Show MoreMetadata
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.
Published in: 2005 IEEE Congress on Evolutionary Computation
Date of Conference: 02-05 September 2005
Date Added to IEEE Xplore: 12 December 2005
Print ISBN:0-7803-9363-5
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