Dec 21, 2022
Chair of Big Data Analytics in Transportation meets ERCIS in Münster
Socializing at one of Münster's Christmas markets
On Monday, December 12, 2022, members of WWU Münster met guests from TU Dresden, Universiteit Leiden, Universiteit Twente, GESIS and TH Köln. The host was the professorship for "Data Science: Statistics and Optimization" (Prof. Dr. Heike Trautmann, WWU Münster).
Prof. Dr. Kerschke, who was the first PhD student of the hosting professorship in 2017 and has been a personal member of ERCIS since 2021, thus consolidated the cooperation of the Dresden professorship for "Big Data Analytics in Transportation" in the ERCIS network (European Research Center for Information Systems). This is a network of research institutions and companies that research and/or use information systems.
Already in the past as well as currently, members of the chair have collaborated with other participants. The following results were published in 2022:
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Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review (2022). Applied Sciences, 12 (18), 9094.
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Heins, J., Bossek J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2022). A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection. Theoretical Computer Science, 940 (B), 123-145.
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Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In: International Conference on Parallel Problem Solving from Nature (192-206). Springer, Cham.
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Kerschke, P., Preuss, M. (11. Sep. 2022). Exploratory Landscape Analysis [Tutorial]. 17th International Conference on Parallel Problem Solving from Nature, Dortmund.
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Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: International Conference on Parallel Problem Solving from Nature (3-17). Springer, Cham.
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Schäpermeier, L., Grimme, C. & Kerschke, P. (2022). Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems. IEEE Transactions on Evolutionary Computation, 26 (6), 1306-1320 .
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Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’22) (592-600). ACM.
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Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In: International Conference on Parallel Problem Solving from Nature (575-589). Springer, Cham.
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Seiler, M. V., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’22) (657-665). ACM.
We will continue to pursue collaborations in the fields of optimization, algorithm selection, and machine learning in the future.