A Preliminary Study on Adaptive Evolution Control Using Rank Correlation for Surrogate-Assisted Evolutionary Computation

Abstract

This article describes how surrogate-assisted evolutionary computation (SAEC) has widely applied to approximate expensive optimization problems, which require much computational time such as hours for one solution evaluation. SAEC may potentially also reduce the processing time of inexpensive optimization problems wherein solutions are evaluated within a few seconds or minutes. To achieve this, the approximation model construction for an objective function should be iterated as few times as possible during optimization. Therefore, this article proposes an adaptive evolution control mechanism for SAEC using rank correlations between actually evaluated and approximately evaluated values of the objective function. These correlations are then used to adaptively switch the approximation and actual evaluation phases, reducing the number of runs required to learn the approximation model. Experiments show that the proposed method could successfully reduce the processing time in some benchmark functions even under inexpensive scenario.