Optimizing expensive-to-evaluate black-box functions of discrete (and potentially
continuous) design parameters is a ubiquitous problem in scientific and engineering …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine
machine learning and optimisation to solve expensive optimisation problems. This type of …

This study targets the mixed-integer black-box optimization (MI-BBO) problem where
continuous and integer variables should be optimized simultaneously. The CMA-ES, our …

Recently, there has been a growing interest for mixed-categorical meta-models based on
Gaussian process (GP) surrogates. In this setting, several existing approaches use different …

In the coming 6G communications, the internet of things (IoT) will be a fundamental enabler
for ubiquitous environment perception, which requires the IoT sensors to consume near-zero …

In the context of the ever-evolving 5G landscape, where network management and control
are paramount, a new Radio Access Network (RAN) as emerged. This innovative RAN offers …

Résumé D e nos jours, un intérêt significatif et croissant pour améliorer les processus de
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …

Surrogate algorithms such as Bayesian optimisation are especially designed for black-box
optimisation problems with expensive objectives, such as hyperparameter tuning or …

Expensive optimization problems are characterized by the significant amount of time and
resources needed to determine the quality of potential solutions. This poses severe …