title: A modified estimation distribution algorithm based on extreme elitism.
abstract: An existing estimation distribution algorithm (EDA) with univariate marginal Gaussian model was improved by designing and incorporating an extreme elitism selection method. This selection method highlighted the effect of a few top best solutions in the evolution and advanced EDA to form a primary evolution direction and obtain a fast convergence rate. Simultaneously, this selection can also keep the population diversity to make EDA avoid premature convergence. Then the modified EDA was tested by means of benchmark low-dimensional and high-dimensional optimization problems to illustrate the gains in using this extreme elitism selection. Besides, no-free-lunch theorem was implemented in the analysis of the effect of this new selection on EDAs.
authors.affiliation: Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver BC, V6T 1Z4, Canada. Electronic address: shujun.gao@mech.ubc.ca.
authors.forename: Shujun
authors.initials: S
authors.lastname: Gao
authors.affiliation: Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver BC, V6T 1Z4, Canada. Electronic address: desilva@mech.ubc.ca.
authors.forename: Clarence W
authors.initials: CW
authors.lastname: de Silva
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D016011:Normal Distribution
keywords: Estimation distribution algorithm; Gaussian model; No-free-lunch theorem; Top best solutions