J. Rieskamp, L. Hofeditz, M. Mirbabaie, and S. Stieglitz, “Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research,” presented at the Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.

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 .

J. Bossek and D. Sudholt, “Do Additional Target Points Speed Up Evolutionary Algorithms?,”10.1016/j.tcs.2023.113757__Theoretical Computer Science, p. 113757, 2023, doi:10.1016/j.tcs.2023.113757 .

L. Moura, A. Nakic, P. Östergård, A. Wassermann, and C. Weiß, Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161), vol. 13, Issue 4. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, pp. 40–57.

A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem,”10.1007/978-3-031-14714-2_17__in Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 237–249, doi:10.1007/978-3-031-14714-2_17 .

S. Li, C. Markarian, F. Meyer auf der Heide, and P. Podlipyan, “A continuous strategy for collisionless gathering,” Theoretical Computer Science, vol. 852, pp. 41–60, 2021.

S. Li, F. Meyer auf der Heide, and P. Podlipyan, “The impact of the Gabriel subgraph of the visibility graph on the gathering of mobile autonomous robots,” Theoretical Computer Science, vol. 852, pp. 29–40, 2021.

C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,”https://doi.org/10.1016/j.cor.2021.105489__Computers &; Operations Research, vol. 136, p. 105489, 2021, doi:https://doi.org/10.1016/j.cor.2021.105489 .

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 and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.

J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,”10.1145/3449726.3463165__in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021, pp. 1423–1432, doi:10.1145/3449726.3463165 .

A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,”10.1145/3449639.3459384__in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 600–608, doi:10.1145/3449639.3459384 .

A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,”10.1145/3449639.3459385__in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 261–269, doi:10.1145/3449639.3459385 .

A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.

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. 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, 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, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions,”10.1007/978-3-030-58112-1_24__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 346–359, doi:10.1007/978-3-030-58112-1_24 .