Self-adaptive differential evolution algorithm for numerical optimization

Download PDF
Open Webpage

A. Kai Qin, Ponnuthurai N. Suganthan

Published: 2005, Last Modified: 31 Aug 2024Congress on Evolutionary Computation 2005EveryoneRevisionsBibTeXCC BY-SA 4.0
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.