Gebken, B. (2024). A note on the convergence of deterministic gradient sampling in nonsmooth optimization. Computational Optimization and Applications.https://doi.org/10.1007/s10589-024-00552-0 

Bick, C., &; von der Gracht, S. (2024). Heteroclinic dynamics in network dynamical systems with higher-order interactions. Journal of Complex Networks, 12(2).https://doi.org/10.1093/comnet/cnae009 

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 

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. Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139.https://doi.org/10.1145/3594805.3607136 

Bossek, J., Neumann, A., &; Neumann, F. (2023). On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 248–256.https://doi.org/10.1145/3583131.3590384 

Tao, Y., &; Winkler, M. (2023). Global smooth solutions in a three-dimensional cross-diffusive SIS epidemic model with saturated taxis at large densities. Evolution Equations and Control Theory, 12(6), 1676–1687.https://doi.org/10.3934/eect.2023031 

Caillau, J.-B., Djema, W., Gouzé, J.-L., Maslovskaya, S., &; Pomet, J.-B. (2022). Turnpike Property in Optimal Microbial Metabolite Production. Journal of Optimization Theory and Applications.https://doi.org/10.1007/s10957-022-02023-0 

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 

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 

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 

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 

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.

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 

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 

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 

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 

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 

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 

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 

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