Classification- and Regression-Assisted Differential Evolution for Computationally Expensive Problems

Differential Evolution (DE) has been well accepted as an effective evolutionary optimization technique. However, it usually involves a large number of fltness evaluations to obtain a satisfactory solution. This disadvantage severely restricts its application to computationally expensive problems, for which a single fltness evaluation can be highly time-consuming. In the past decade, a lot of investigations have been conducted to incorporate a surrogate model into an evolutionary algorithm (EA) to alleviate its computational burden in this scenario. However, only limited work was devoted to DE. More importantly, although various types of surrogate models, such as regression, ranking, and classiflcation models, have been investigated separately, none of them consistently outperforms others. In this paper, we propose to construct a surrogate model by combining both regression and classiflcation techniques. It is shown that due to the speciflc selection strategy of DE, a synergy can be established between these two types of models, and leads to a surrogate model that is more appropriate for DE. A novel surrogate model-assisted DE, named Classiflcation-and Regression-Assisted DE (CRADE) is proposed on this basis. Experimental studies are carried out on a set of 16 benchmark functions, and CRADE has shown signiflcant superiority over DE-assisted with only regression or classiflcation models. Further comparison to three state-of-the-art DE variants, i.e., DE with global and local neighborhoods (DEGL), JADE, and composite DE (CoDE), also demonstrates the superiority of CRADE.