Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …

Benchmarking heuristic algorithms is vital to understand under which conditions and on
what kind of problems certain algorithms perform well. In most current research into heuristic …

Extending a recent suggestion to generate new instances for numerical black-box
optimization benchmarking by interpolating pairs of the well-established BBOB functions …

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 …

Empirical data plays an important role in evolutionary computation research. To make better
use of the available data, ontologies have been proposed in the literature to organize their …

The application of semantic technologies, particularly ontologies, in the realm of multi-
objective evolutionary algorithms is overlook despite their effectiveness in knowledge …

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

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

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance
can simultaneously belong to multiple classes. MLC is increasingly gaining interest in …

The performance of automated algorithm selection (AAS) strongly depends on the portfolio
of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires …