Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Vinzent Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2024.

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.

Runtime Analysis of Quality Diversity Algorithms
J. Bossek, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 1546–1554.

Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space
A. Marrero, E. Segredo, E. Hart, J. Bossek, A. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 312–320.

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

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem
J. Bossek, C. Grimme, Evolutionary Computation (2023) 1–35.

A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M.V. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.

Exploring the Feature Space of TSP Instances Using Quality Diversity
J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 186–194.

Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA
J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 824–842.

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems
J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 192–206.

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.

Process-Oriented Stream Classification Pipeline: A Literature Review
L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 9094.

On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.

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.

Exact Counting and~Sampling of Optima for the Knapsack Problem
J. Bossek, A. Neumann, F. Neumann, in: Learning and Intelligent Optimization, Springer-Verlag, Berlin, Heidelberg, 2021, pp. 40–54.

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.

On the Potential of Normalized TSP Features for Automated Algorithm Selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.

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.