title: Gaussian Bare-Bones Differential Evolution.
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.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.forename: Shahryar
authors.initials: S
authors.lastname: Rahnamayan
authors.forename: Hui
authors.initials: H
authors.lastname: Sun
authors.forename: Mahamed G H
authors.initials: MG
authors.lastname: Omran