Gaussian Bare-Bones Differential Evolution
IEEE Transactions on CyberneticsPublished 20 September 2012
Hui Wang, Shahryar Rahnamayan, Hui Sun, Mahamed G. H. Omran
Citations268
SJR quartileQ1
SJR score4.45
SNIP3.22
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TL;DR
A Gaussian bare-bones DE and its modified version (MGBDE) are proposed which are almost parameter free and indicate that the MGBDE performs significantly better than, or at least comparable to, several state-of-the-art DE variants and some existing bare-bone algorithms.
Abstract
Differential evolution (DE) is a well-known algorithm for global optimization over continuous search spaces. However, choosing the optimal control parameters is a challenging task because they are problem oriented. In order to minimize the effects of the control parameters, a Gaussian bare-bones DE (GBDE) and its modified version (MGBDE) are proposed which are almost parameter free. To verify the performance of our approaches, 30 benchmark functions and two real-world problems are utilized. Conducted experiments indicate that the MGBDE performs significantly better than, or at least comparable to, several state-of-the-art DE variants and some existing bare-bones algorithms.
Keywords
Computer Science
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