Surrogate-assisted, or meta-model based evolutionary computation uses efficient
computational models, often known as surrogates or meta-models, for approximating the …

Abstract The Expected Hypervolume Improvement (EHVI) is a frequently used infill criterion
in Multi-Objective Bayesian Global Optimization (MOBGO), due to its good ability to lead the …

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 …

In the field of multi-objective optimization algorithms, multi-objective Bayesian Global
Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective …

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 …

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 …

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms
gleaned from biological evolution theory. They have been successfully applied to a wide …

We introduce MISO, the mixed-integer surrogate optimization framework. MISO aims at
solving computationally expensive black-box optimization problems with mixed-integer …

As potent approaches for addressing computationally expensive optimization problems,
surrogate-assisted evolutionary algorithms (SAEAs) have garnered increasing attention …