| 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 | 49 | 2021 |
| 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 | 46 | 2022 |
| Iohexperimenter: Benchmarking platform for iterative optimization heuristics J de Nobel, F Ye, D Vermetten, H Wang, C Doerr, T Bäck Evolutionary Computation, 1-6, 2024 | 40 | 2024 |
| Evolutionary algorithms for parameter optimization—thirty years later THW Bäck, AV Kononova, B van Stein, H Wang, KA Antonov, ... Evolutionary Computation 31 (2), 81-122, 2023 | 34 | 2023 |
| Combining supervised and unsupervised machine learning methods for phenotypic functional genomics screening WA Omta, RG van Heesbeen, I Shen, J de Nobel, D Robers, ... Slas Discovery: Advancing the Science of Drug Discovery 25 (6), 655-664, 2020 | 21 | 2020 |
| 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 | 12 | 2022 |
| Explorative data analysis of time series based algorithm features of CMA-ES variants J de Nobel, H Wang, T Baeck Proceedings of the Genetic and Evolutionary Computation Conference, 510-518, 2021 | 12 | 2021 |
| 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 | 10 | 2021 |
| Improving comprehension efficiency of high content screening data through interactive visualizations WA Omta, J Nobel, J Klumperman, DA Egan, MR Spruit, MJS Brinkhuis Assay and drug development technologies 15 (6), 247-256, 2017 | 8 | 2017 |
| Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem SS Ahouei, J de Nobel, A Neumann, T Bäck, F Neumann arXiv preprint arXiv:2405.18772, 2024 | 6 | 2024 |
| Optimizing stimulus energy for cochlear implants with a machine learning model of the auditory nerve J de Nobel, AV Kononova, JJ Briaire, JHM Frijns, THW Bäck Hearing Research 432, 108741, 2023 | 6 | 2023 |
| When to be discrete: analyzing algorithm performance on discretized continuous problems A Thomaser, J De Nobel, D Vermetten, F Ye, T Bäck, A Kononova Proceedings of the Genetic and Evolutionary Computation Conference, 856-863, 2023 | 4 | 2023 |
| Computing star discrepancies with numerical black-box optimization algorithms F Clément, D Vermetten, J De Nobel, AD Jesus, L Paquete, C Doerr Proceedings of the Genetic and Evolutionary Computation Conference, 1330-1338, 2023 | 4 | 2023 |
| Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler F Neumann, A Neumann, C Qian, AV Do, J de Nobel, D Vermetten, ... 2023 IEEE Congress on Evolutionary Computation (CEC), 1-9, 2023 | 4 | 2023 |
| Evolutionary algorithms for parameter optimization: thirty years later THW Back, AV Kononova, N Stein, H Wang, K Antonov, RT Kalkreuth, ... Evolutionary Computation 31 (2), 81-122, 2023 | 3 | 2023 |
| Avoiding redundant restarts in multimodal global optimization J de Nobel, D Vermetten, AV Kononova, OM Shir, T Bäck International Conference on Parallel Problem Solving from Nature, 268-283, 2024 | 2 | 2024 |
| Sampling in CMA-ES: Low Numbers of Low Discrepancy Points J de Nobel, D Vermetten, THW Bäck, AV Kononova arXiv preprint arXiv:2409.15941, 2024 | 1 | 2024 |
| What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms F Ye, F Neumann, J de Nobel, A Neumann, T Bäck Proceedings of the Genetic and Evolutionary Computation Conference, 787-795, 2024 | 1 | 2024 |
| Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling J de Nobel, SSM Martens, JJ Briaire, THW Bäck, AV Kononova, ... Hearing Research 447, 109011, 2024 | 1 | 2024 |
| Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks A Wong, J de Nobel, T Bäck, A Plaat, AV Kononova arXiv preprint arXiv:2402.06912, 2024 | 1 | 2024 |
Thomas BäckProfessor of Computer Science, Leiden University; Chief Scientist, NORCE Research Centre, NorwayEmail yang diverifikasi di liacs.leidenuniv.nl