It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …

Choosing the best-performing optimizer (s) out of a portfolio of optimization algorithms is
usually a difficult and complex task. It gets even worse, if the underlying functions are …

Multi-solution problems extensively exist in practice. Particularly, the traveling salesman
problem (TSP) may possess multiple shortest tours, from which travelers can choose one …

The herein proposed Python package pflacco provides a set of numerical features to
characterize single-objective continuous and constrained optimization problems. Thereby …

❖ Gabriela Ochoa is a Professor in Computing Science at the University of Stirling, Scotland.
She holds a PhD from the University of Sussex, UK. Her research interests include …

In benchmarking theory, creating a comprehensive and uniformly distributed set of problems
is a crucial first step to designing a good benchmark. However, this step is also one of the …

Oftentimes the characteristics of real-world engineering optimization problems are not well
understood. In this paper, we introduce an approach for characterizing highly nonlinear and …

The parameters of the quantum circuit in a variational quantum algorithm induce a
landscape that contains the relevant information regarding its optimization hardness. In this …