Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem M Seiler, J Pohl, J Bossek, P Kerschke, H Trautmann
International Conference on Parallel Problem Solving from Nature, 48-64, 2020
32 2020 FakeYou!-a gamified approach for building and evaluating resilience against fake news L Clever, D Assenmacher, K Müller, MV Seiler, DM Riehle, M Preuss, ...
Multidisciplinary international symposium on disinformation in open online …, 2020
29 2020 D Assenmacher, M Niemann, K Müller, M Seiler, DM Riehle, H Trautmann
Thirty-fifth conference on neural information processing systems datasets …, 2021
26 2021 A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes MV Seiler, RP Prager, P Kerschke, H Trautmann
Proceedings of the Genetic and Evolutionary Computation Conference, 657-665, 2022
21 2022 Automated algorithm selection in single-objective continuous optimization: a comparative study of deep learning and landscape analysis methods RP Prager, MV Seiler, H Trautmann, P Kerschke
International Conference on Parallel Problem Solving from Nature, 3-17, 2022
20 2022 A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 J Pohl, MV Seiler, D Assenmacher, C Grimme
Available at SSRN 4066543, 2022
20 2022 Towards feature-free automated algorithm selection for single-objective continuous black-box optimization RP Prager, MV Seiler, H Trautmann, P Kerschke
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2021
18 2021 A study on the effects of normalized TSP features for automated algorithm selection J Heins, J Bossek, J Pohl, M Seiler, H Trautmann, P Kerschke
Theoretical Computer Science 940, 123-145, 2023
14 2023 Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems MV Seiler, P Kerschke, H Trautmann
arXiv preprint arXiv:2401.01192, 2024
10 2024 Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. JS Pohl, D Assenmacher, MV Seiler, H Trautmann, C Grimme
ICWSM Workshops, 2022
6 2022 On the potential of normalized tsp features for automated algorithm selection J Heins, J Bossek, J Pohl, M Seiler, H Trautmann, P Kerschke
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic …, 2021
5 2021 Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP MV Seiler, J Rook, J Heins, OL Preuß, J Bossek, H Trautmann
2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361-368, 2023
4 2023 Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis S Noteboom, M Seiler, C Chien, RP Rane, F Barkhof, EMM Strijbis, F Paul, ...
Journal of Neurology 271 (8), 5577-5589, 2024
2 2024 Synergies of deep and classical exploratory landscape features for automated algorithm selection M Seiler, U Škvorc, C Doerr, H Trautmann
International Conference on Learning and Intelligent Optimization, 361-376, 2024
1 2024 Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization? G Cenikj, G Petelin, M Seiler, N Cenikj, T Eftimov
arXiv preprint arXiv:2501.17663, 2025
2025 Pioneering new paths: the role of generative modelling in neurological disease research M Seiler, K Ritter
Pflügers Archiv-European Journal of Physiology, 1-19, 2024
2024 Learned Features vs. Classical ELA on Affine BBOB Functions M Seiler, U Škvorc, G Cenikj, C Doerr, H Trautmann
International Conference on Parallel Problem Solving from Nature, 137-153, 2024
2024 Potential and Challenges of Feature-free Automated Algorithm Selection and Configuration MV Seiler
Universität Münster, 2023
2023 Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries M Seiler, H Trautmann, P Kerschke
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
2020 Machine learning based prediction of clinical progression in multiple sclerosis S Noteboom, M Seiler, C Chien, RP Rane, EMM Strijbis, F Paul, ...
