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33 Publications
2025 | Conference Paper | LibreCat-ID: 56298 | 
Gerlach, R., von der Gracht, S., Hahn, C., Harbig, J., & Kling, P. (2025). Symmetry Preservation in Swarms of Oblivious Robots with Limited Visibility. In S. Bonomi, L. Galletta, Etienne Rivière, & Valerio Schiavoni (Eds.), 28th International Conference on Principles of Distributed Systems (OPODIS 2024) (Vol. 324). Schloss Dagstuhl -- Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2024.13
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| arXiv
2024 | Journal Article | LibreCat-ID: 54548
Prager, R. P., & Trautmann, H. (2024). Exploratory Landscape Analysis for Mixed-Variable Problems. IEEE Transactions on Evolutionary Computation, 1–1. https://doi.org/10.1109/TEVC.2024.3399560
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2023 | Conference Paper | LibreCat-ID: 37312 |
Leffrang, D., Bösch, K., & Müller, O. (2023). Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences.
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2023 | Conference Paper | LibreCat-ID: 50121
Leffrang, D. (2023). AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization. International Conference on Information Systems, 10.
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2023 | Conference Paper | LibreCat-ID: 50118
Leffrang, D. (2023). The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization. Wirtschaftsinformatik Conference, 19.
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2023 | Journal Article | LibreCat-ID: 46310
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019
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2023 | Book Chapter | LibreCat-ID: 54428
Schulte Eickholt, S. (2023). Normative Diversität? Nahe Zukunft bei Sibylle Berg und Marc-Uwe Kling. In L. Cosan, O. K. Bazarkaya, & H. Tekin (Eds.), Germanistik im Wandel 1. Neue Einsichten und Perspektiven in der Literaturwissenschaft (pp. 65–75). Logos.
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2021 | Book Chapter | LibreCat-ID: 48881
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
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2020 | Conference Paper | LibreCat-ID: 48897
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
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2020 | Journal Article | LibreCat-ID: 48848
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
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2020 | Journal Article | LibreCat-ID: 46334
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
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2019 | Journal Article | LibreCat-ID: 6512 |
Rauchecker, G., & Schryen, G. (2019). Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm. Computers & Operations Research, (104), 338–357.
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2019 | Conference Paper | LibreCat-ID: 48875
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 215–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_19
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2018 | Conference Paper | LibreCat-ID: 3422
Robinson, P., Scheideler, C., & Setzer, A. (n.d.). Breaking the $\tilde\Omega(\sqrt{n})$ Barrier: Fast Consensus under a Late Adversary. In Proceedings of the 30th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA). Wien. https://doi.org/10.1145/3210377.3210399
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2018 | Conference Paper | LibreCat-ID: 48885
Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
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2018 | Journal Article | LibreCat-ID: 48884
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
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2018 | Conference Paper | LibreCat-ID: 1590
Lass, M., Mohr, S., Wiebeler, H., Kühne, T., & Plessl, C. (2018). A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices. Proc. Platform for Advanced Scientific Computing (PASC) Conference. Platform for Advanced Scientific Computing Conference (PASC), Basel, Switzerland. https://doi.org/10.1145/3218176.3218231
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| arXiv
2017 | Conference Paper | LibreCat-ID: 17652
Polevoy, G., Trajanovski, S., Grosso, P., & de Laat, C. (2017). Filtering Undesirable Flows in Networks. In Combinatorial Optimization and Applications: 11th International Conference, COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part I (pp. 3–17). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-71150-8_1
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2016 | Conference Paper | LibreCat-ID: 48873
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
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2014 | Conference Paper | LibreCat-ID: 9879
Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In Prognostics and Health Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
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