Pascal Kerschke

TL;DR: This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection.

TL;DR: In this article, the authors introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature, and demonstrate the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

TL;DR: In this paper, an algorithm selection model for continuous black-box optimization problems is presented, based on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications.

TL;DR: Flacco is introduced, an R-package for feature-based landscape analysis of continuous and constrained optimization problems that offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package.

TL;DR: This work directly compares five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrates complementary performance, in that different instances may be solved most effectively by different algorithms.