Former Research Assistant
Data Science: Statistics and Optimization
Leonardo Campus 3
48149
Münster
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Leonardo Campus 3
48149
Münster
Prager, R., & Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In Correia, J., Smith, S., & Qaddoura, R. (Eds.), Applications of Evolutionary Computation (pp. 411–425). Cham: Springer Nature Switzerland.
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Prager, R. P., & Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation. (accepted / in press (not yet published))
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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 Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Cham: Springer International Publishing.
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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 Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). Cham: Springer International Publishing.
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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 -, (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 657–665). New York, NY, USA: ACM Press.
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Prager, R. P., Moritz, V. H., & Pascal, (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA.
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Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 996–1003.
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Prager, R. P., Troost, L., Brüggenjürgen, S., Melhart, D., Yannakakis, G., & Preuss, M. (2019). An Experiment on Game Facet Combination. In Proceedings of the IEEE Conference on Games, London.
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