Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …

Introducing new algorithmic ideas is a key part of the continuous improvement of existing
optimization algorithms. However, when introducing a new component into an existing …

Automated algorithm selection promises to support the user in the decisive task of selecting
a most suitable algorithm for a given problem. A common component of these machine …

Feature-based algorithm selection aims to automatically find the best one from a portfolio of
optimization algorithms on an unseen problem based on its landscape features. Feature …

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used
iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many …

Selecting the most suitable algorithm and determining its hyperparameters for a given
optimization problem is a challenging task. Accurately predicting how well a certain …

The conflict between computational budget and quality of found solutions is crucial when
dealing with expensive black-box optimization problems from the industry. We show that …

In this paper, we rely on previous work proposing a modularized version of CMA-ES, which
captures several alterations to the conventional CMA-ES developed in recent years. Each …