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Diederick Vermetten
Diederick Vermetten
Verified email at liacs.leidenuniv.nl
Title
Cited by
Cited by
Year
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics
H Wang, D Vermetten, F Ye, C Doerr, T Bäck
ACM Transactions on Evolutionary Learning and Optimization 2 (1), 1-29, 2022
40*2022
Performance comparison of optimization methods on variational quantum algorithms
X Bonet-Monroig, H Wang, D Vermetten, B Senjean, C Moussa, T Bäck, ...
Physical Review A 107 (3), 032407, 2023
312023
Online selection of CMA-ES variants
D Vermetten, S van Rijn, T Bäck, C Doerr
Proceedings of the Genetic and Evolutionary Computation Conference, 951-959, 2019
262019
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules
J de Nobel, D Vermetten, H Wang, C Doerr, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
242021
Per-run algorithm selection with warm-starting using trajectory-based features
A Kostovska, A Jankovic, D Vermetten, J de Nobel, H Wang, T Eftimov, ...
International Conference on Parallel Problem Solving from Nature, 46-60, 2022
192022
IOHexperimenter: Benchmarking platform for iterative optimization heuristics
J de Nobel, F Ye, D Vermetten, H Wang, C Doerr, T Bäck
arXiv preprint arXiv:2111.04077, 2021
132021
Integrated vs. sequential approaches for selecting and tuning CMA-ES variants
D Vermetten, H Wang, C Doerr, T Back
ACM Genetic and Evolutionary Computation Conference (GECCO'20), 2020
132020
Towards dynamic algorithm selection for numerical black-box optimization: investigating BBOB as a use case
D Vermetten, H Wang, T Bäck, C Doerr
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 654-662, 2020
132020
Is there anisotropy in structural bias?
D Vermetten, AV Kononova, F Caraffini, H Wang, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
102021
Bias: a toolbox for benchmarking structural bias in the continuous domain
D Vermetten, B van Stein, F Caraffini, LL Minku, AV Kononova
IEEE Transactions on Evolutionary Computation 26 (6), 1380-1393, 2022
72022
Analysis of structural bias in differential evolution configurations
D Vermetten, B van Stein, AV Kononova, F Caraffini
Differential Evolution: From Theory to Practice, 1-22, 2022
72022
Option: optimization algorithm benchmarking ontology
A Kostovska, D Vermetten, C Doerr, S Džeroski, P Panov, T Eftimov
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
72021
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
H Wang, D Vermetten, F Ye, C Doerr, T Bäck
arXiv preprint arXiv:2007.03953, 2020
72020
BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
FX Long, D Vermetten, B van Stein, AV Kononova
International Conference on the Applications of Evolutionary Computation …, 2023
62023
The importance of landscape features for performance prediction of modular CMA-ES variants
A Kostovska, D Vermetten, S Džeroski, C Doerr, P Korosec, T Eftimov
Proceedings of the Genetic and Evolutionary Computation Conference, 648-656, 2022
62022
Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms
D Vermetten, H Wang, M López-Ibañez, C Doerr, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference, 867-875, 2022
62022
The importance of being constrained: Dealing with infeasible solutions in differential evolution and beyond
AV Kononova, D Vermetten, F Caraffini, MA Mitran, D Zaharie
arXiv preprint arXiv:2203.03512, 2022
62022
Performance comparison of optimization methods on variational quantum algorithms. 2021
X Bonet-Monroig, H Wang, D Vermetten, B Senjean, C Moussa, T Bäck, ...
URL: https://arxiv. org/abs/2111.13454. doi 10, 2021
52021
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. CoRR abs/2111.04077 (2021)
J de Nobel, F Ye, D Vermetten, H Wang, C Doerr, T Bäck
arXiv preprint arXiv:2111.04077, 2021
52021
Modular Differential Evolution
D Vermetten, F Caraffini, AV Kononova, T Bäck
arXiv preprint arXiv:2304.09524, 2023
32023
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