The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

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

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …

In dealing with expensive constrained multiobjective optimization problems using surrogate-
assisted evolutionary algorithms, it is a great challenge to reduce the negative impact …

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to
be optimized. A number of efficient decomposition-based evolutionary algorithms have been …

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

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling
computationally expensive multiobjective optimization problems (EMOPs), as the surrogate …

We propose a new surrogate-assisted evolutionary algorithm for expensive multiobjective
optimization. Two classification-based surrogate models are used, which can predict the …

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 …