In recent years, the integration of intelligent industrial process monitoring, quality prediction,
and predictive maintenance solutions has garnered significant attention, driven by rapid …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

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

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

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 …

In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …

While Reinforcement Learning (RL) has made great strides towards solving increasingly
complicated problems, many algorithms are still brittle to even slight environmental changes …

Cutting planes (cuts) play an important role in solving mixed-integer linear programs
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …

Although Reinforcement Learning (RL) has shown to be capable of producing impressive
results, its use is limited by the impact of its hyperparameters on performance. This often …