Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Genetic algorithms constitute a family of black-box optimization algorithms, which take
inspiration from the principles of biological evolution. While they provide a general-purpose …

Optimizing functions without access to gradients is the remit of black-box methods such as
evolution strategies. While highly general, their learning dynamics are often times heuristic …

Abstract Black-Box Optimization (BBO) is increasingly vital for addressing complex real-
world optimization challenges, where traditional methods fall short due to their reliance on …

Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A
popular algorithm configuration tuning paradigm is dynamic algorithm configuration (DAC) …

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 …

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

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

Simulated annealing (SA) is a stochastic global optimisation metaheuristic applicable to a
wide range of discrete and continuous variable problems. Despite its simplicity, SA hinges …

AutoML tackles the problem of automatically configuring machine learning pipelines to
specific data analysis problems. These pipelines may include methods for preprocessing …