Performance analysis of a (1+1) surrogate-assisted evolutionary algorithm

Abstract

Although surrogate-assisted evolutionary algorithms have been widely utilized in the science and engineering fields, efficient management of surrogates is still a challenging task because of the complicated action mechanisms of surrogates. Trying to investigate how surrogates influence the convergence of evolutionary algorithms, this paper performs an analysis of a (1+1) surrogate-assisted evolutionary algorithm with Gaussian mutations, where a linear surrogate model is employed to approximately evaluate the candidate solutions. Numerical results demonstrate that the linear surrogate model greatly improves performance of evolutionary algorithms on a unimodal problem when the standard deviation of Gaussian mutation σ is small. However, when σ is set big, the improving functions of linear surrogate models are not significant. However, for a multi-modal problem, the positive function of the linear surrogate model is not significant, because the employed Gaussian mutation cannot keep a balance between exploration and exploitation. © 2014 Springer International Publishing Switzerland.