Abstract
This chapter presents surrogate-assisted evolutionary algorithms for single-objective optimization that employ multiple surrogates. Multiple surrogates can not only improve the prediction performance and estimate the degree of prediction uncertainty, but also capture both global and local features of the fitness landscape. The multiple surrogates can be used as an ensemble, in parallel, hierarchically, or in an interleaving way. Finally, we describe a method for adaptively selecting one surrogate at a particular search stage from a pool of surrogates according to their performance in the history.