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10 Publications
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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2024 | Conference Paper | LibreCat-ID: 56277
Kilsbach, S., & Michel, N. (2024). Computer-Based Generation of Learner-Sensitive Feedback to Argumentative Learner Texts. Proceedings of the Tenth Conference of the International Society for the Study of Argumentation. Tenth Conference of the International Society for the Study of Argumentation, Leiden.
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2023 | Book Chapter | LibreCat-ID: 52662
Nachtigall, M., Schlichtig, M., & Bodden, E. (2023). Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale. In Software Engineering 2023 (pp. 95–96). Gesellschaft für Informatik e.V.
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2023 | Conference Paper | LibreCat-ID: 52816
Gräßler, I., & Hieb, M. (2023). Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing. Lectures, 253–524. https://doi.org/10.5162/smsi2023/d7.4
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2022 | Conference Paper | LibreCat-ID: 32410
Nachtigall, M., Schlichtig, M., & Bodden, E. (2022). A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools. Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, 532–543. https://doi.org/10.1145/3533767
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2021 | Journal Article | LibreCat-ID: 21004
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276
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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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2018 | Conference Paper | LibreCat-ID: 3852 | 
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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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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