Although experiments are the gold standard for establishing causality, several threats can
undermine the internal validity of experimental findings. In this article, we first discuss these …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

This paper deals with the problem of coordinating a truck and multiple heterogeneous
unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. Existing …

Parameter tuning, that is, to find appropriate parameter settings (or configurations) of
algorithms so that their performance is optimized, is an important task in the development …

Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-
order) aerodynamic models or flight testing are some of the fundamental but complex steps …

In recent years, the field of combinatorial optimization has witnessed a true tsunami of
“novel” metaheuristic methods, most of them based on a metaphor of some natural or man …

The performance of many machine learning methods depends critically on hyperparameter
settings. Sophisticated Bayesian optimization methods have recently achieved considerable …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

State-of-the-art algorithms for hard computational problems often expose many parameters
that can be modified to improve empirical performance. However, manually exploring the …

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