Abstract
Gaussian processes are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because Gaussian processes are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing Gaussian processes may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a Gaussian process and a homogeneous ensemble for infill sampling criteria in evolutionary multi-objective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with Gaussian processes and much more scalable in computational complexity to the increase in search dimension.