12 papers
On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
Koen van der Blom, Diederick Vermetten
Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance. This decision is base…
Similarity-based Portfolio Construction for Black-box Optimization
Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr
In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks:…
Exploration of Pareto-preserving Search Space Transformations in Multi-objective Test Functions
Diederick Vermetten, Jeroen Rook
Benchmark problems are an important tool for gaining understanding of optimization algorithms. Since algorithms often aim to perform well on benchmarks, biases in benchmark design…
How Sequential Algorithm Portfolios can benefit Black Box Optimization
Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr
In typical black-box optimization applications, the available computational budget is often allocated to a single algorithm, typically chosen based on user preference with limited…
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers
Diederick Vermetten, Catalin-Viorel Dinu, Marcus Gallagher
One key challenge in optimization is the selection of a suitable set of benchmark problems. A common goal is to find functions which are representative of a class of real-world opt…