| Transfer Learning of Surrogate Models via Domain Affine Transformation S Pan, D Vermetten, M López-Ibáñez, T Bäck, H Wang Proceedings of the Genetic and Evolutionary Computation Conference, 385-393, 2024 | | 2024 |
| Optimizing Causal Interventions in Hybrid Bayesian Networks M Vonk, D Vermetten, J de Nobel, S Brand, N Malekovic, T Bäck, ... | | 2024 |
| Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks A Nikolikj, A Kostovska, D Vermetten, C Doerr, T Eftimov arXiv preprint arXiv:2405.11964, 2024 | 1 | 2024 |
| Avoiding Redundant Restarts in Multimodal Global Optimization J de Nobel, D Vermetten, AV Kononova, OM Shir, T Bäck arXiv preprint arXiv:2405.01226, 2024 | | 2024 |
| Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler D Vermetten, J Lengler, D Rusin, T Bäck, C Doerr arXiv preprint arXiv:2404.15837, 2024 | | 2024 |
| Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization K Dietrich, D Vermetten, C Doerr, P Kerschke arXiv preprint arXiv:2404.07539, 2024 | | 2024 |
| Using the empirical attainment function for analyzing single-objective black-box optimization algorithms M López-Ibáñez, D Vermetten, J Dreo, C Doerr arXiv preprint arXiv:2404.02031, 2024 | 4 | 2024 |
| The importance of being constrained: Dealing with infeasible solutions in differential evolution and beyond AV Kononova, D Vermetten, F Caraffini, MA Mitran, D Zaharie Evolutionary Computation 32 (1), 3-48, 2024 | 12 | 2024 |
| Large-scale Benchmarking of Metaphor-based Optimization Heuristics D Vermetten, C Doerr, H Wang, AV Kononova, T Bäck arXiv preprint arXiv:2402.09800, 2024 | 1 | 2024 |
| 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 | 31 | 2024 |
| Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems H Yin, D Vermetten, F Ye, THW Bäck, AV Kononova arXiv preprint arXiv:2402.07654, 2024 | | 2024 |
| Explainable benchmarking for iterative optimization heuristics N van Stein, D Vermetten, AV Kononova, T Bäck arXiv preprint arXiv:2401.17842, 2024 | 6 | 2024 |
| llmendinger, R., & Konono a DL Vermetten, M López-Ibánez, O Mersmann V, 2024 | | 2024 |
| Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332) D Vermetten, MS Krejca, M Lindauer, M López-Ibáñez, KM Malan Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2024 | | 2024 |
| Ma-bbob: A problem generator for black-box optimization using affine combinations and shifts D Vermetten, F Ye, T Bäck, C Doerr ACM Transactions on Evolutionary Learning, 2024 | 5 | 2024 |
| PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization A Kostovska, G Cenikj, D Vermetten, A Jankovic, A Nikolikj, U Skvorc, ... International Conference on Automated Machine Learning, 11/1-17, 2023 | | 2023 |
| MA-BBOB: Many-affine combinations of BBOB functions for evaluating automl approaches in noiseless numerical black-box optimization contexts D Vermetten, F Ye, T Bäck, C Doerr International Conference on Automated Machine Learning, 7/1-14, 2023 | 6 | 2023 |
| General Boolean Function Benchmark Suite R Kalkreuth, Z Vašíček, J Husa, D Vermetten, F Ye, T Bäck Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023 | 1 | 2023 |
| Comparing algorithm selection approaches on black-box optimization problems A Kostovska, A Jankovic, D Vermetten, S Džeroski, T Eftimov, C Doerr Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 7 | 2023 |
| Assessing the generalizability of a performance predictive model A Nikolikj, G Cenikj, G Ispirova, D Vermetten, RD Lang, AP Engelbrecht, ... Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 3 | 2023 |
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de