We consider the problem of optimizing hybrid structures (mixture of discrete and continuous
input variables) via expensive black-box function evaluations. This problem arises in many …

Humans perform Bayesian inference in a wide variety of tasks, which can help people make
selection decisions effectively and therefore enhances learning efficiency and accuracy …

Many real-world combinatorial optimization problems have both expensive objective and
constraint functions. Although surrogate models for the discrete decision variables can be …

We introduce a novel surrogate-assisted Genetic Algorithm (GA) for expensive optimization
of problems with discrete categorical variables. Specifically, we leverage the strengths of the …

Surrogate-modeling is about formulating quick-to-evaluate mathematical models, to
approximate black-box and time-consuming computations or simulation tasks. Although …

The aim of this paper is to study surrogate-assisted algorithms for expensive multiobjective
combinatorial optimization problems. Targeting pseudo-boolean domains, we provide a fine …

Comparing combinatorial optimization problems is a difficult task. They are defined using
different criteria and terms: weights, flows, distances, etc. In spite of this apparent …

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 …

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-
world optimization problems when fitness evaluations are expensive, limiting the number of …

We consider the design and analysis of surrogate-assisted algorithms for expensive multi-
objective combinatorial optimization. Focusing on pseudo-boolean functions, we leverage …