Bossek J, Neumann A, Neumann F. On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’23. Association for Computing Machinery; 2023:248–256. doi:10.1145/3583131.3590384

Bossek J, Sudholt D. Do Additional Target Points Speed Up Evolutionary Algorithms? Theoretical Computer Science. Published online 2023:113757. doi:10.1016/j.tcs.2023.113757

Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tu\'76 sar T, eds. Parallel Problem Solving from Nature (PPSN XVII). Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249. doi:10.1007/978-3-031-14714-2_17

Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association for Computing Machinery; 2021:556–564. doi:10.1145/3449639.3459364

Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:10.1145/3449639.3459363

Bossek J, Sudholt D. Do Additional Optima Speed up Evolutionary Algorithms? In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery; 2021:1–11.

Bossek J, Wagner M. Generating Instances with Performance Differences for More than Just Two Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO’21. Association for Computing Machinery; 2021:1423–1432. doi:10.1145/3449726.3463165

Nikfarjam A, Bossek J, Neumann A, Neumann F. Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’21. Association for Computing Machinery; 2021:600–608. doi:10.1145/3449639.3459384

Neumann A, Bossek J, Neumann F. Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’21. Association for Computing Machinery; 2021:261–269. doi:10.1145/3449639.3459385

Nikfarjam A, Bossek J, Neumann A, Neumann F. 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. Association for Computing Machinery; 2021:1–11.

Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica. 2021;83(10):3148–3179. doi:10.1007/s00453-021-00838-3

Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174

Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243

Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146

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

Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:10.1145/3377930.3389844

Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168

Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’19. Association for Computing Machinery; 2019:1443–1451. doi:10.1145/3321707.3321792

Bossek J, Bischl B, Wagner T, Rudolph G. Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’15. Association for Computing Machinery; 2015:1319–1326. doi:10.1145/2739480.2754673