Variational quantum algorithms (VQAs) offer a promising path toward using near-term
quantum hardware for applications in academic and industrial research. These algorithms …

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 …

Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …

The selection mechanism of genetic algorithms can play a key role in leading the
optimization process towards suitable solutions of a given problem, as their application can …

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 …

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

Many fields of computational science advance through improvements in the algorithms used
for solving key problems. These advancements are often facilitated by benchmarks and …

Automated Machine Learning (AutoML) is used more than ever before to support users in
determining efficient hyperparameters, neural architectures, or even full machine learning …

In the discrete domain, self-adjusting parameters of evolutionary algorithms (EAs) has
emerged as a fruitful research area with many runtime analyses showing that self-adjusting …

Finding the best configuration of algorithms' hyperparameters for a given optimization
problem is an important task in evolutionary computation. We compare in this work the …