Classification-assisted Differential Evolution for computationally expensive problems

Like most Evolutionary Algorithms (EAs), Differential Evolution (DE) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This is an obstacle for applying DE to computationally expensive problems. Many previous studies have been carried out to develop surrogate assisted approaches for EAs to reduce the number of real fitness evaluations. Existing methods typically build surrogates with either regression or ranking methods. However, due to the pairwise selection scheme of DE, it is more appropriate to formulate the construction of surrogate as a classification problem rather than a regression or ranking problem. Hence, we propose a classification-assisted DE in this paper. Experimental studies showed that the classification-assisted DE has great potential when compared to the DE that uses regression or ranking techniques to build surrogates.