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12 Publications


2024 | Conference Paper | LibreCat-ID: 54643
Learned Features vs. Classical ELA on Affine BBOB Functions
M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann, in: M. Affenzeller, S. Winkler, A. Kononova, H. Trautmann, T. Tušar, P. Machado, T. Baeck (Eds.), Parallel Problem Solving from Nature — PPSN XVIII, Springer International Publishing, Cham, 2024, pp. 1–14.
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2024 | Conference Paper | LibreCat-ID: 52749
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2024.
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2023 | Journal Article | LibreCat-ID: 46310
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 (2023) 123–145.
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2023 | Conference Paper | LibreCat-ID: 48898
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.
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2022 | Conference Paper | LibreCat-ID: 46307
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
M. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665.
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2022 | Conference Paper | LibreCat-ID: 46304
Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods
R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17.
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2022 | Conference Paper | LibreCat-ID: 46303
Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches
J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in: for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.
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2021 | Conference Paper | LibreCat-ID: 46315
Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization
R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8.
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2021 | Conference Paper | LibreCat-ID: 46312
RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets
D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D.M. Riehle, H. Trautmann, in: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 2021, pp. 1–14.
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2021 | Conference Paper | LibreCat-ID: 46313
On the Potential of Normalized TSP Features for Automated Algorithm Selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), Association for Computing Machinery, Dornbirn, Austria, 2021, pp. 1–15.
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2020 | Conference Paper | LibreCat-ID: 46331
Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries
M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.
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2020 | Conference Paper | LibreCat-ID: 46330
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, in: T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 2020, pp. 48–64.
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