Choosing a set of benchmark problems is often a key component of any empirical evaluation
of iterative optimization heuristics. In continuous, single-objective optimization, several sets …

The task of selecting the best optimization algorithm for a particular problem is known as
algorithm selection (AS). This involves training a model using landscape characteristics to …

Within the optimization community, the question of how to generate new optimization
problems has been gaining traction in recent years. Within topics such as instance space …

Artificial intelligence methods are being increasingly applied across various domains, but
their often opaque nature has raised concerns about accountability and trust. In response …

Solving real-world engineering optimization problems, such as automotive crashworthiness
optimization, is extremely challenging, because the problem characteristics are oftentimes …

The recently proposed MA-BBOB function generator provides a way to create numerical
black-box benchmark problems based on the well-established BBOB suite. Initial studies on …

Automated algorithm selection has proven to be effective to improve optimization
performance by using machine learning to select the best-performing algorithm for the …

AI methods are finding an increasing number of applications, but their often black-box nature
has raised concerns about accountability and trust. The field of explainable artificial …

Recent approaches to training algorithm selectors in the black-box optimisation domain
have advocated for the use of training data that is algorithm-centric in order to encapsulate …

Exploratory Landscape Analysis (ELA) features are the most common choice for
representing single-objective con-tinuous optimization problem instances in Algorithm …