prof.dr. H. Trautmann (Heike)


Over mij

I am Guest Professor of Data Science: Statistics and Optimization at the University of Twente and Professor of Machine Learning and Optimization at the Department of Computer Science, Paderborn University, Germany. I am member of the European Research Center for Information Systems (ERCIS) and head of the ERCIS competence center Social Media Analytics as well as the ERCIS research cluster 'Data Science and AI'.

My research mainly focuses on Data Science, Data Stream Mining, Social Media Analytics, Algorithmization and Social Interaction, Automated Algorithm Selection and Configuration as well as (Multiobjective) Evolutionary Optimization. 


Engineering & Materials Science
Clustering Algorithms
Deep Learning
Machine Learning
Multiobjective Optimization
Vehicle Routing
Social Sciences
Artificial Intelligence


  • Paderborn University , Germany
    Professor of Machine Learning and Optimisation


Please have a look at my complete publication list :-)


Preuß, O. L. , Rook, J. , & Trautmann, H. (2024). On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems. In Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (pp. 305-321). (Lecture Notes in Computer Science; Vol. 14634). https://doi.org/10.1007/978-3-031-56852-7_20
Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P. , Trautmann, H., & Grimme, C. (2023). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 22(2), 271–285. Advance online publication. https://doi.org/10.1007/s11047-022-09919-w
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
Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P. , Trautmann, H., & Mersmann, O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In FOGA 2023: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 129-139). Association for Computing Machinery. https://doi.org/10.1145/3594805.3607136
Schäpermeier, L., Kerschke, P., Grimme, C. , & Trautmann, H. (2023). Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, & I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings (pp. 291-304). (Lecture Notes in Computer Science; Vol. 13970). Springer. https://doi.org/10.1007/978-3-031-27250-9_21
Prager, R. P. , & Trautmann, H. (2023). Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In GECCO 2023 Companion: Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 451-454). Association for Computing Machinery. https://doi.org/10.1145/3583133.3590757
Prager, R. P. , & Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation : 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (pp. 411-425). (Lecture Notes in Computer Science; Vol. 13989). Springer. https://doi.org/10.1007/978-3-031-30229-9_27
Seiler, M. V. , Rook, J., Heins, J., Preuß, O. L., Bossek, J. , & Trautmann, H. (2023). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 361-368). (IEEE Symposium Series on Computational Intelligence; Vol. 2023). IEEE. https://doi.org/10.1109/SSCI52147.2023.10372008
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 GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 657-665). Association for Computing Machinery. https://doi.org/10.1145/3512290.3528834
Assenmacher, D. , & Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In N. T. Nguyen, B. Trawiński, N. T. Nguyen, T. K. Tran, U. Tukayev, T.-P. Hong, & E. Szczerbicki (Eds.), Intelligent Information and Database Systems: 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28–30, 2022, Proceedings, Part I (pp. 3-16). (Lecture Notes in Computer Science; Vol. 13757). Springer. https://doi.org/10.1007/978-3-031-21743-2_1
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 G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I (pp. 3-17). (Lecture Notes in Computer Science; Vol. 13398). Springer. https://doi.org/10.1007/978-3-031-14714-2_1
Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B. , Trautmann, H., & Kerschke, P. (2022). HPO × ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I (pp. 575-589). (Lecture Notes in Computer Science; Vol. 13398). Springer. https://doi.org/10.1007/978-3-031-14714-2_40
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P. , & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences (Switzerland), 12(18), Article 9094. https://doi.org/10.3390/app12189094
Leszkiewicz, A. , Bucur, D., Grimme, C., Michalski, R., Clever, L., Pohl, J. , Rook, J., Bossek, J., Preuss, M., Squillero, G., Quer, S., Calabrese, A., Iacca, G. , Kizgin, D. H. , & Trautmann, H. (2022). Social Influence Analysis (SIA) in Online Social Networks. Paper presented at 4th Multidisciplinary International Symposium, MISDOOM 2022 , Boise, Idaho, United States.
Heins, J. , Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J. , & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature: PPSN XVII (pp. 192-206). (Lecture Notes in Computer Science; Vol. 13398). Springer. https://doi.org/10.1007/978-3-031-14714-2_14

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I am study coordinator of the executive master program "Data Science" (University of Münster Professional School). This is a joint program of UT and the University of Münster.

In my previous role of Vice Dean of Internationalization at the University of Münster, I supported two Double Degree Agreements between UT and UM:

Verbonden aan Opleidingen



Vakken Collegejaar  2022/2023

Current Projects



Universiteit Twente
Drienerlolaan 5
7522 NB Enschede

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Universiteit Twente
Postbus 217
7500 AE Enschede

Overige contactinformatie

Department of Information Systems
Data Science: Statistics and Optimization 
University of Münster
Leonardo-Campus 3         
48149 Münster, Germany

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