文件名称:Gaussian-Bare-Bones-Differential-Evolution
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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.
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.
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Gaussian Bare-Bones Differential Evolution.pdf