Most existing work on evolutionary optimization assumes that there are analytic functions for
evaluating the objectives and constraints. In the real world, however, the objective or …

The use of surrogate models is a standard method for dealing with complex real-world
optimization problems. The first surrogate models were applied to continuous optimization …

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

Reducing building energy demand is a crucial part of the global response to climate change,
and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an …

Surrogate-assisted optimization has attracted much attention due to its superiority in solving
expensive optimization problems. However, relatively little work has been dedicated to …

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 …

The optimization of expensive to evaluate, black-box, mixed-variable functions, ie functions
that have continuous and discrete inputs, is a difficult and yet pervasive problem in science …

Surrogate assisted meta-heuristic algorithms have received increasing attention over the
past years due to the fact that many real-world optimization problems are computationally …

Abstract In continuous optimisation, Surrogate Models (SMs) are often indispensable
components of optimisation algorithms aimed at tackling real-world problems whose …

In real-world optimization, it is often expensive to evaluate the quality of a candidate
solution. The costs may be due to run-time of a complex computer simulation, time required …