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Diederick Vermetten
Diederick Vermetten
Verified email at liacs.leidenuniv.nl
Title
Cited by
Cited by
Year
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
232019
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
212022
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
142021
Performance comparison of optimization methods on variational quantum algorithms
X Bonet-Monroig, H Wang, D Vermetten, B Senjean, C Moussa, T Bäck, ...
arXiv preprint arXiv:2111.13454, 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
112020
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
92020
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
72021
IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic. CoRR abs/2007.03953 (2020)
H Wang, D Vermetten, F Ye, C Doerr, T Bäck
arXiv preprint arXiv:2007.03953, 2020
52020
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
42022
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
42021
Per-run algorithm selection with warm-starting using trajectory-based features
A Kostovska, A Jankovic, D Vermetten, J de Nobel, H Wang, T Eftimov, ...
Parallel Problem Solving from Nature–PPSN XVII: 17th International …, 2022
32022
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
32022
Squirrel: A switching hyperparameter optimizer
N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ...
arXiv preprint arXiv:2012.08180, 2020
32020
Trajectory-based algorithm selection with warm-starting
A Jankovic, D Vermetten, A Kostovska, J de Nobel, T Eftimov, C Doerr
2022 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2022
22022
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
22022
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
22021
Using structural bias to analyse the behaviour of modular CMA-ES
D Vermetten, F Caraffini, B van Stein, AV Kononova
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022
12022
Chaining of numerical black-box algorithms: warm-starting and switching points
D Schröder, D Vermetten, H Wang, C Doerr, T Bäck
arXiv preprint arXiv:2204.06539, 2022
12022
Sequential vs. integrated algorithm selection and configuration: A case study for the modular cma-es
D Vermetten, H Wang, C Doerr, T Bäck
arXiv preprint arXiv:1912.05899, 2019
12019
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
A Kostovska, D Vermetten, S Džeroski, P Panov, T Eftimov, C Doerr
arXiv preprint arXiv:2301.09876, 2023
2023
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