Gaussian Bare-Bones Differential Evolution



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.