Benchmarking is a cornerstone in the analysis and development of computational methods,
especially in the field of evolutionary computation, where theoretical analysis of the …

In the power and energy systems area, a progressive increase of literature contributions that
contain applications of metaheuristic algorithms is occurring. In many cases, these …

We introduce COCO, an open-source platform for Comparing Continuous Optimizers in a
black-box setting. COCO aims at automatizing the tedious and repetitive task of …

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 …

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …

Abstract Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-
RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine …

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

Automated benchmarking environments aim to support researchers in understanding how
different algorithms perform on different types of optimization problems. Such comparisons …

The performance of an algorithm often critically depends on its parameter configuration.
While a variety of automated algorithm configuration methods have been proposed to …

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