An Evolutionary Approach for the Computation of -Locally Optimal Solutions for Multi-Objective Multimodal Optimization
C. Hernández, A.E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2025) 1–1.

Finding ?5-Locally Optimal Solutions for Multi-Objective Multimodal Optimization
A.E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H. Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2024) 1–1.

On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 248–256.

Do Additional Target Points Speed Up Evolutionary Algorithms?
J. Bossek, D. Sudholt, Theoretical Computer Science (2023) 113757.

Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161)
L. Moura, A. Nakic, P. Östergård, A. Wassermann, C. Weiß, Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161), Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.

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

Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\'76 sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.

A continuous strategy for collisionless gathering
S. Li, C. Markarian, F. Meyer auf der Heide, P. Podlipyan, Theoretical Computer Science 852 (2021) 41–60.

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

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization
C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H. Wang, M. Emmerich, Computers &; Operations Research 136 (2021) 105489.

Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms
J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 556–564.

Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem
J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 198–206.

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

Generating Instances with Performance Differences for More than Just Two Algorithms
J. Bossek, M. Wagner, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1423–1432.

Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem
A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 600–608.

Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions
A. Neumann, J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 261–269.

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

Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.

More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization
J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1277–1285.

The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1286–1294.