Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

Following decades of sustained improvement, metaheuristics are one of the great success
stories of optimization research. However, in order for research in metaheuristics to avoid …

To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial
component of machine learning and its applications. Over the last years, the number of …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

We present concepts and recipes for the anytime performance assessment when
benchmarking optimization algorithms in a blackbox scenario. We consider runtime …

Evolutionary algorithms have been shown to be very effective in solving complex
optimization problems. This has driven the research community in the development of novel …

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 …

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about
the reproducibility and replicability of such studies have increased in recent times, reflecting …

Various benchmark sets have already been proposed to facilitate comparison between
metaheuristics, or Evolutionary Algorithms. During the competition, typically algorithms are …

We present IOHexperimenter, the experimentation module of the IOHprofiler project.
IOHexperimenter aims at providing an easy-to-use and customizable toolbox for …