Self-adaptive barebones differential evolution

被引：1

作者：

Omran, Mahamed G. H. ^{[1
]}

Engelbrecht, Andries P. ^{[2
]}

Salman, Ayed ^{[3
]}

机构：

[1] Gulf Univ Sci & Technol, Dept Comp Sci, Kuwait, Kuwait

[2] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa

[3] Kuwait Univ, Dept Comp Engn, Safat 13060, Kuwait

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424834

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 new version of DE which eliminates the need for manual parameter tuning is proposed. The performance of the proposed approach is investigated and compared with other well-known approaches. The results show that the new algorithm provides good performance when applied to multimodal problems with the added advantage that no parameter tuning is needed.

引用

页码：2858 / +

页数：4

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