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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Self-adaptive Differential Evolution
verfasst von : Mahamed G. H. Omran, Ayed Salman, Andries P. Engelbrecht
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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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.