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J. Bossek, A. Neumann, and F. Neumann, “On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem,”10.1145/3583131.3590384__in Proceedings of the Genetic and Evolutionary Computation Conference, 2023, pp. 248–256, doi:10.1145/3583131.3590384 .

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J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,”10.1007/978-3-031-14714-2_14__in Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 192–206, doi:10.1007/978-3-031-14714-2_14 .

J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,”10.1145/3520304.3528998__in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359, doi:10.1145/3520304.3528998 .

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi:10.1109/tpami.2021.3051276 .

R. Schemmel, V. Krieger, T. Hemsel, and W. Sextro, “Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization,”https://doi.org/10.1016/j.microrel.2021.114077__Microelectronics Reliability, vol. 119, p. 114077, 2021, doi:https://doi.org/10.1016/j.microrel.2021.114077 .

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J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,”10.1145/3449639.3459364__in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 556–564, doi:10.1145/3449639.3459364 .

J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,”10.1145/3449639.3459363__in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 198–206, doi:10.1145/3449639.3459363 .

J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,”10.1007/s00453-021-00838-3__Algorithmica, vol. 83, no. 10, pp. 3148–3179, 2021, doi:10.1007/s00453-021-00838-3 .

J. Castenow, B. Feldkord, T. Knollmann, M. Malatyali, and F. Meyer auf der Heide, “The Online Multi-Commodity Facility Location Problem,” in Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures, 2020.

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J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,”https://doi.org/10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, p. 105901, 2020, doi:https://doi.org/10.1016/j.asoc.2019.105901 .

J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,”10.1145/3377930.3390174__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285, doi:10.1145/3377930.3390174 .

J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,”10.1007/978-3-030-58112-1_8__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8 .

J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics,”10.1145/3377930.3390243__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243 .

J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,”10.1145/3377930.3390146__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 166–174, doi:10.1145/3377930.3390146 .

J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB,”10.1145/3377930.3390155__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 778–786, doi:10.1145/3377930.3390155 .

J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,”10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901 .

B. Hippert, A. Uhde, and S. T. Wengerek, “Portfolio Benefits of Adding Corporate Credit Default Swap Indices: Evidence from North America and Europe,”https://doi.org/10.1007/s11147-018-9148-8__Review of Derivatives Research , vol. 22, no. 2, pp. 203–259, 2019, doi:https://doi.org/10.1007/s11147-018-9148-8 .

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J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,”10.1145/3299904.3340307__in Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019, pp. 58–71, doi:10.1145/3299904.3340307 .

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J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem,”10.1145/3321707.3321818__in Proceedings of the Genetic and Evolutionary Computation Conference, 2019, pp. 516–523, doi:10.1145/3321707.3321818 .

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P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,”10.1145/3205651.3208233__in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233 .

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J. Bossek and C. Grimme, “A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem,”10.1109/SSCI.2017.8285183__in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285183 .

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J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,”10.1007/978-3-319-49130-1_1__in Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1 .

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J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement,”10.1145/2739480.2754673__in Proceedings of the Genetic and Evolutionary Computation Conference, 2015, pp. 1319–1326, doi:10.1145/2739480.2754673 .

S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann, “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle,”10.1145/2739480.2754705__in Proceedings of the Genetic and Evolutionary Computation Conference , 2015, pp. 425–432, doi:10.1145/2739480.2754705 .