Self-adaptive differential evolution

Authors:
Mahamed G. H. Omran;Ayed Salman;Andries P. Engelbrecht
Affiliations:
Faculty of Computing & IT, Arab Open University, Kuwait;Computer Engineering Department, Kuwait University, Kuwait;Department of Computer Science, University of Pretoria, Pretoria, South Africa
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

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.