Self-adaptive differential evolution

被引：0
|
作者：
Omran, MGH [1 ]
Salman, A
Engelbrecht, AP
机构：
[1] Arab Open Univ, Fac Comp & IT, Kuwait, Kuwait
[2] Kuwait Univ, Dept Comp Engn, Kuwait, Kuwait
[3] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa
来源：
COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS | 2005年 / 3801卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) is proposed where parameter tuning is not required. The performance of SDE is investigated and compared with other versions of DE. The experiments conducted show that SDE outperformed the other DE versions in all the benchmark functions.
引用
收藏
页码：192 / 199
页数：8
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