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

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 …

Most optimization problems of practical significance are typically solved by highly
configurable parameterized algorithms. To achieve the best performance on a problem …

Benchmarking heuristic algorithms is vital to understand under which conditions and on
what kind of problems certain algorithms perform well. In most current research into heuristic …

The repeated solution of large-scale optimization problems arises frequently in process
systems engineering tasks. Decomposition-based solution methods have been widely used …

In many practical applications, usually, similar optimisation problems or scenarios
repeatedly appear. Learning from previous problem-solving experiences can help adjust …

In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to
numerically characterize, in particular, single-objective continuous optimization problems …

To achieve the best performance, automatic theorem provers often rely on schedules of
diverse proving strategies to be tried out (either sequentially or in parallel) on a given …

The parameter configuration problem consists of finding a parameter configuration that gives
a particular algorithm the best performance. This paper introduces a new multi-phase tuner …

We consider the combinatorial bandits problem with semi-bandit feedback under finite
sampling budget constraints, in which the learner can carry out its action only for a limited …