Gene Sorting in Differential Evolution

Authors:
Remi Tassing;Desheng Wang;Yongli Yang;Guangxi Zhu
Affiliations:
Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and Wuhan National Laboratory for Optoelectronics, Huazhong University of ...
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

Gene sorting is a method proposed in this article that consists of ordering trial vector's component in differential evolution (DE). This method tends to significantly increase the convergence speed of DE with just a little modification on the original algorithm. A benchmark set of 18 functions is used for comparing both algorithms. Most importantly, the proposed methods can be incorporated in other variants of DE to further increase their respective speeds; Iterated Function System Based Adaptive Differential Evolution (IFDE) is used in this paper as a variant example and it is about 5 times faster for 30-dimension problems.