| Metaheuristics “in the large” J Swan, S Adriaensen, AEI Brownlee, K Hammond, CG Johnson, A Kheiri, ... European Journal of Operational Research 297 (2), 393-406, 2022 | 136 | 2022 |
| Automated dynamic algorithm configuration S Adriaensen, A Biedenkapp, G Shala, N Awad, T Eimer, M Lindauer, ... Journal of Artificial Intelligence Research 75, 1633-1699, 2022 | 70 | 2022 |
| A research agenda for metaheuristic standardization J Swan, S Adriaensen, M Bishr, EK Burke, JA Clark, P De Causmaecker, ... Proceedings of the XI metaheuristics international conference, 1-3, 2015 | 64 | 2015 |
| Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks S Adriaensen, H Rakotoarison, S Müller, F Hutter Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS …, 2023 | 57 | 2023 |
| Learning step-size adaptation in CMA-ES G Shala, A Biedenkapp, N Awad, S Adriaensen, M Lindauer, F Hutter International Conference on Parallel Problem Solving from Nature, 691-706, 2020 | 53 | 2020 |
| Fair-share ILS: a simple state-of-the-art iterated local search hyperheuristic S Adriaensen, T Brys, A Nowé Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014 | 42 | 2014 |
| DACBench: A benchmark library for dynamic algorithm configuration T Eimer, A Biedenkapp, M Reimer, S Adriaensen, F Hutter, M Lindauer Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 39 | 2021 |
| A benchmark set extension and comparative study for the hyflex framework S Adriaensen, G Ochoa, A Nowé 2015 IEEE Congress on Evolutionary Computation (CEC), 784-791, 2015 | 35 | 2015 |
| In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization H Rakotoarison, S Adriaensen, N Mallik, S Garibov, E Bergman, F Hutter Forty-first International Conference on Machine Learning, 2024 | 27 | 2024 |
| Case study: An analysis of accidental complexity in a state-of-the-art hyper-heuristic for HyFlex S Adriaensen, A Nowé 2016 IEEE Congress on Evolutionary Computation (CEC), 1485-1492, 2016 | 20 | 2016 |
| Designing reusable metaheuristic methods: A semi-automated approach S Adriaensen, T Brys, A Nowé 2014 IEEE congress on evolutionary computation (CEC), 2969-2976, 2014 | 20 | 2014 |
| Towards a White Box Approach to Automated Algorithm Design. S Adriaensen, A Nowé IJCAI, 554-560, 2016 | 18 | 2016 |
| From Epoch to Sample Size: Developing New Data-driven Priors for Learning Curve Prior-Fitted Networks TJ Viering, S Adriaensen, H Rakotoarison, F Hutter AutoML Conference 2024 (Workshop Track), 2024 | 9 | 2024 |
| Extending the “open-closed principle” to automated algorithm configuration J Swan, S Adriænsen, AD Barwell, K Hammond, DR White Evolutionary Computation 27 (1), 173-193, 2019 | 6 | 2019 |
| Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics I Das, M Safari, S Adriaensen, F Hutter arXiv preprint arXiv:2502.03654, 2025 | 3 | 2025 |
| An importance sampling approach to the estimation of algorithm performance in automated algorithm design S Adriaensen, F Moons, A Nowé International Conference on Learning and Intelligent Optimization, 3-17, 2017 | 2 | 2017 |
| Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks D Lee, DB Lee, S Adriaensen, J Lee, SJ Hwang, F Hutter, SJ Kim, HB Lee arXiv preprint arXiv:2505.23032, 2025 | 1 | 2025 |
| α-PFN: In-Context Learning Entropy Search TJ Viering, S Adriaensen, H Rakotoarison, S Müller, C Hvarfner, F Hutter, ... Frontiers in Probabilistic Inference: Learning meets Sampling, 2025 | 1 | 2025 |
| On the Semi-automated Design of Reusable Heuristics S Adriaensen Vrije Universiteit Brussel, 2018 | 1 | 2018 |
| Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning DB Lee, AS Zhang, B Kim, J Park, S Adriaensen, J Lee, SJ Hwang, ... arXiv preprint arXiv:2510.21379, 2025 | | 2025 |
Frank HutterPrior Labs; ELLIS Institute Tübingen; University of FreiburgVerified email at cs.uni-freiburg.de
