Title :
Self-adaptive differential evolution algorithm for numerical optimization
Author :
Qin, A.K. ; Suganthan, P.N.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
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.
Keywords :
evolutionary computation; learning (artificial intelligence); optimisation; self-adjusting systems; learning strategy; numerical optimization; parameter settings; real parameter optimization; self-adaptive differential evolution algorithm; Chromium; Communication system control; Genetic mutations; Mechanical engineering; Pattern recognition; Space technology; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554904
