Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for
solving expensive optimization problems where only a small number of real fitness …

Surrogate models have shown to be effective in assisting metaheuristic algorithms for
solving computationally expensive complex optimization problems. The effectiveness of …

Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve
computationally expensive optimization problems. The critical idea of SAEAs is to combine …

Abstract Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of
optimisers developed to undertake problems with computationally expensive fitness …

GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-
objective optimization that is designed to obtain good non-dominated solutions to black box …

This paper proposes a novel algorithm named surrogate ensemble assisted differential
evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale …

Complexity in solving real‐world multicriteria optimization problems often stems from the fact
that complex, expensive, and/or time‐consuming simulation tools or physical experiments …

As we know, the performance of differential evolution (DE) highly depends on the mutation
strategy. However, it is difficult to choose a suitable mutation strategy for a specific problem …

In engineering practice, most optimization problems have multiple objectives, which are
usually in a form of expensive black-box functions. The multiobjective efficient global …

This chapter provides a short overview of the most significant research work that has been
conducted regarding the solution of computationally expensive multi-objective optimization …