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Caruso, C., &; Seifert, A. (2022). Pädagogische Kompetenz als Ausgangspunkt beruflichen Könnens!? Ergebnisse der Kompetenzmessung angehender Lehrkräfte unter Berücksichtigung individueller Voraussetzungen. Religionspädagogische Beiträge. Journal for Religion in Education , 45(1), 3–15.https://doi.org/10.20377/rpb-101 
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Bonnard, B., Rouot, J., &; Wembe Moafo, B. E. (2022). Accessibility properties of abnormal geodesics in optimal control illustrated by two case studies. Mathematical Control and Related Fields, 0(0), 0–0.https://doi.org/10.3934/mcrf.2022052 
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Schröer, F., &; Tenberge, C. (2022). How to enable pre-service teachers to design technological teaching and learning inclusively? – On the nature and consideration of basic needs in teacher training. In D. Gill, J. Tuff, T. Kennedy, S. Pendergast, &; S. Jamil (Eds.), PAT - PATT on the Edge Technology, Innovation and Education (pp. 49–57).
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Constantiou, I., Mukkamala, A., Sjöklint, M., &; Trier, M. (2022). Engaging with self-tracking applications: how do users respond to their performance data? European Journal of Information Systems, 1–21.https://doi.org/10.1080/0960085x.2022.2081096 
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Schön, O., Götte, R.-S., &; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12), 19–24.https://doi.org/10.1016/j.ifacol.2022.07.282 
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Philipo, G. H., Kakande, J. N., &; Krauter, S. (2022). Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies, 15(14), Article 5215.https://doi.org/10.3390/en15145215 
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Philipo, G. H., Kakande, J. N., &; Krauter, S. (2022). Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies, 15(14), Article 5215.https://doi.org/10.3390/en15145215 
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Hesse, M., Hunstig, M., Timmermann, J., &; Trächtler, A. (2022). Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 383–394.
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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 G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &; T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 192–206). Springer International Publishing.https://doi.org/10.1007/978-3-031-14714-2_14 
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Rook, J., Trautmann, H., Bossek, J., &; Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 356–359.https://doi.org/10.1145/3520304.3528998 
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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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Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., &; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. Transactions on Network and Service Management.https://doi.org/10.1109/TNSM.2021.3076503 
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Schneider, S. B., Karl, H., Khalili, R., &; Hecker, A. (2021). DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.
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Bonanati, S., &; Buhl, H. M. (2021). The digital home learning environment and its relation to children’s ICT self-efficacy. Learning Environments Research, 25(2), 485–505.https://doi.org/10.1007/s10984-021-09377-8 
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Ichev, R., Koren, J., Kosi, U., Sitar Sustar, K., &; Valentincic, A. (2021). Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection of Risk.
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Uhe, B., Kuball, C.-M., Merklein, M., &; Meschut, G. (2021). Self-Piercing Riveting Using Rivets Made of Stainless Steel with High Strain Hardening. In G. Daehn, J. Cao, B. Kinsey, E. Tekkaya, A. Vivek, &; Y. Yoshida (Eds.), Forming the Future - Proceedings of the 13th International Conference on the Technology of Plasticity. The Minerals, Metals &; Materials Series. (pp. 1495–1506). Springer.https://doi.org/10.1007/978-3-030-75381-8_124 
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Uhe, B., Kuball, C.-M., Merklein, M., &; Meschut, G. (2021). Strength of self-piercing riveted Joints with conventional Rivets and Rivets made of High Nitrogen Steel. 24th International Conference on Material Forming (ESAFORM), Liège, Belgien.https://doi.org/10.25518/esaform21.1911 
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Schemmel, R., Krieger, V., Hemsel, T., &; Sextro, W. (2021). Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization. Microelectronics Reliability, 119, 114077.https://doi.org/10.1016/j.microrel.2021.114077 
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Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., &; Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers &; Operations Research, 136, 105489.https://doi.org/10.1016/j.cor.2021.105489 
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Bossek, J., Neumann, A., &; Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 556–564.https://doi.org/10.1145/3449639.3459364 
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Bossek, J., &; Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 198–206.https://doi.org/10.1145/3449639.3459363 
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Bossek, J., Neumann, F., Peng, P., &; Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica, 83(10), 3148–3179.https://doi.org/10.1007/s00453-021-00838-3 
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Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., &; Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In IEEE International Conference on Network and Service Management (CNSM). IEEE.
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Kuball, C.-M., Uhe, B., Meschut, G., &; Merklein, M. (Eds.). (2020). Process design for the forming of semi-tubular self-piercing rivets made of high nitrogen steel (Vol. 50, pp. 280–285).https://doi.org/10.1016/j.promfg.2020.08.052 
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Castenow, J., Feldkord, B., Knollmann, T., Malatyali, M., &; Meyer auf der Heide, F. (2020). The Online Multi-Commodity Facility Location Problem. In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures.https://doi.org/10.1145/3350755.3400281 
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arXiv: 2005.08391

Otroshi, M., Rossel, M., &; Meschut, G. (2020). Stress state dependent damage modeling of self-pierce riveting process simulation using GISSMO damage model. Journal of Advanced Joining Processes, 1.https://doi.org/10.1016/j.jajp.2020.100015 
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Caruso, C., Adammek, C., Bonanati, S., &; Wiescholek, S. (2020). Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen. Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion, 3(1), 18–33.https://doi.org/10.4119/hlz-2540 
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Bonnard, B., Cots, O., &; Wembe Moafo, B. E. (2020). A Zermelo navigation problem with a vortex singularity. ESAIM: Control, Optimisation and Calculus of Variations, 27, Article .https://doi.org/10.1051/cocv/2020058 
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Kuball, C.-M., Jung, R., Uhe, B., Meschut, G., &; Merklein, M. (Eds.). (2020). Influence of the process temperature on the forming behaviour and the friction during bulk forming of high nitrogen steel (No. 100023; Vol. 1).https://doi.org/10.1016/j.jajp.2020.100023 
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Uhe, B., Kuball, C.-M., Merklein, M., &; Meschut, G. (2020). Improvement of a rivet geometry for the self-piercing riveting of high-strength steel and multi-material joints. Production Engineering, 14, 417–423.https://doi.org/10.1007/s11740-020-00973-w 
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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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Bossek, J., Neumann, F., Peng, P., &; Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.https://doi.org/10.1145/3377930.3390174 
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Bossek, J., Doerr, C., Kerschke, P., Neumann, A., &; Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem Solving from Nature (PPSN XVI), 111–124.https://doi.org/10.1007/978-3-030-58112-1_8 
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Bossek, J., Casel, K., Kerschke, P., &; Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. Proceedings of the Genetic and Evolutionary Computation Conference, 1286–1294.https://doi.org/10.1145/3377930.3390243 
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Bossek, J., Grimme, C., &; Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference, 166–174.https://doi.org/10.1145/3377930.3390146 
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Bossek, J., Doerr, C., &; Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation Conference, 778–786.https://doi.org/10.1145/3377930.3390155 
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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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Hippert, B., Uhde, A., &; Wengerek, S. T. (2019). Portfolio Benefits of Adding Corporate Credit Default Swap Indices: Evidence from North America and Europe. Review of Derivatives Research , 22(2), 203–259.https://doi.org/10.1007/s11147-018-9148-8 
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Bornmann, P. (2019). Modellierung und experimentelle Charakterisierung der Wechselwirkung zwischen Ultraschallwandler und Flüssigkeit in kavitationsbasierten Prozessen. Shaker.
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Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &; Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &; P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Springer International Publishing.https://doi.org/10.1007/978-3-030-12598-1_41 
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Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., &; Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 58–71.https://doi.org/10.1145/3299904.3340307 
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Bossek, J., Neumann, F., Peng, P., &; Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of the Genetic and Evolutionary Computation Conference, 1443–1451.https://doi.org/10.1145/3321707.3321792 
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Bossek, J., Grimme, C., &; Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 516–523.https://doi.org/10.1145/3321707.3321818 
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Feldmann, M., Kolb, C., Scheideler, C., &; Strothmann, T. F. (2018). Self-Stabilizing Supervised Publish-Subscribe Systems. In Proceedings of the 32nd IEEE International Parallel &; Distributed Processing Symposium (IPDPS). Vancouver: IEEE.https://doi.org/10.1109/IPDPS.2018.00114 
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arXiv: 1710.08128

Rauchecker, G., &; Schryen, G. (2018). Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management. Rochester, NY, USA.
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Knollmann, T., &; Scheideler, C. (2018). A Self-Stabilizing Hashed Patricia Trie. In T. Izumi &; P. Kuznetsov (Eds.), Proceedings of the 20th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS) (Vol. 11201). Tokyo: Springer, Cham.https://doi.org/10.1007/978-3-030-03232-6_1 
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arXiv: 1809.04923

Unger, A., Hunstig, M., Meyer, T., Brökelmann, M., &; Sextro, W. (2018). Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges. In In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018 (Vol. Vol. 2018, No. 1, pp. 000572-000577.).https://doi.org/10.4071/2380-4505-2018.1.000572 
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Bertau, M.-C., &; Karsten, A. (2018). Reconsidering interiorization: Self moving across language spacetimes. New Ideas in Psychology, 49, 7–17.https://doi.org/10.1016/j.newideapsych.2017.12.001 
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