85 citations · 897 across the 89 of their papers we have counts for
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Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Ana Kostovska, Anja Jankovic, Diederick Vermetten +3
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms
François Clément, Diederick Vermetten, Jacob de Nobel +3
The star discrepancy is a measure for the regularity of a finite set of points taken from . Low discrepancy point sets are highly relevant for Quasi-Monte Car…
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COm…
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
Gjorgjina Cenikj, Gašper Petelin, Carola Doerr +2
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These feat…
Heuristic Approaches to Obtain Low-Discrepancy Point Sets via Subset Selection
François Clément, Carola Doerr, Luís Paquete
Building upon the exact methods presented in our earlier work [J. Complexity, 2022], we introduce a heuristic approach for the star discrepancy subset selection problem. The heuris…