A fundamental study on adaptive surrogate-assisted evolutionary computation using rank correlation

Surrogate-Assisted Evolutionary Computation (SAEC) has widely applied to approximate an objective function. However, 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 of a fitness function should be iterated as few times as possible during optimization. This paper proposes an adaptive SAEC algorithm using the rank correlations between the 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. It was confirmed experimentally that the proposed method could successfully reduce the processing time in some benchmark functions even under inexpensive scenario.