Publications

Below, you find a chronological list of all my publications. I try to include a freely accessible version of each manuscript where the published version is not open access. In case you need a paper (version) that’s not accessible for you, please get in touch, I’m happy to help!

2026

Schäpermeier, L. & Kerschke, P. (2026). BONO-Bench: A Comprehensive Test Suite for Bi-objective Numerical Optimization with Traceable Pareto Sets. Accepted in ACM Transactions on Evolutionary Learning and Optimization.

• Author version (arXiv)
• bonobench repository: https://github.com/schaepermeier/bonobench

Kononova, A. V., van Stein, N., Mersmann, O., Bäck, T., Bartz-Beielstein, T., Glasmachers, T., Hellwig, M., Krey, S., Kůdela, J., Naujoks, B., Papenmeier, L., Raponi, E., Renau, Q., Rook, J., Schäpermeier, L., Vermetten, D. & Zaharie, D. (2026). Benchmarking that Matters: Rethinking Benchmarking for Practical Impact. Accepted at EvoStar 2026.

• Author version (arXiv)

2025

Eimer, T., Schäpermeier, L., Biedenkapp, A., Tornede, A., Kotthoff, L., Leyman, P., Feurer, M., Eggensperger, K., Maile, K., Tornede, T., Kozak, A., Xue, K., Wever, M., Baratchi, M., Pulatov, D., Trautmann, H., Kashgarani, H. & Lindauer, M. (2025). Best Practices For Empirical Meta-Algorithmic Research Guidelines from the COSEAL Research Network. arXiv preprint arXiv:2512.16491.

• Author version (arXiv)
• Living document repository: https://github.com/coseal/COSEAL-Best-Practices

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

• DOI: https://doi.org/10.1109/TEVC.2025.3637276

Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P., Trautmann, H. & Schütze, O. (2025). Finding ε-locally Optimal Solutions for Multi-objective Multimodal Optimization. In IEEE Transactions on Evolutionary Computation, 29(5), 2019-2031.

• DOI: https://doi.org/10.1109/TEVC.2024.3458855
• Author version

Schäpermeier, L., & Kerschke, P. (2025). R2 v2: The Pareto-compliant R2 Indicator for Better Benchmarking in Bi-objective Optimization. Evolutionary Computation, 1-17.

• DOI: https://doi.org/10.1162/EVCO.a.366
• Author version (arXiv)

Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández Castellanos, C., Kerschke, P., Trautmann, H., & Schütze, O. (2025). Hot off the Press: Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 61-62).

• DOI: https://doi.org/10.1145/3712255.3734260

Schäpermeier, L. (2025). Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1199-1207).

• DOI: https://doi.org/10.1145/3712256.3726408
• Author version (arXiv)

2024

Schäpermeier, L., Kerschke, P. (2024). Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15151. Springer, Cham. Best Paper Award 🥳 (PPSN2024@X)

• DOI: https://doi.org/10.1007/978-3-031-70085-9_13
• Author version (arXiv)
• Poster

Heins, J., Schäpermeier, L., Kerschke, P., Whitley, D. (2024). Dancing to the State of the Art?. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15148. Springer, Cham.

• DOI: https://doi.org/10.1007/978-3-031-70055-2_7
• Author version (arXiv)

2023

Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H. & Mersmann, O. (2023, August). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 129-139).

• DOI: https://doi.org/10.1145/3594805.3607136
• Free access

Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023, March). Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings (pp. 291-304). Cham: Springer Nature Switzerland.

• DOI: https://doi.org/10.1007/978-3-031-27250-9_21
• Author version

2022

Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). Plotting Impossible? Surveying Visualization Methods for Continuous Multi-objective Benchmark Problems. IEEE Transactions on Evolutionary Computation, 26(6), 1306-1320.

• DOI: https://doi.org/10.1109/TEVC.2022.3214894
• Author version

Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., & Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 1-15.

• DOI (open access): https://doi.org/10.1007/s11047-022-09919-w

Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO × ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In Parallel Problem Solving from Nature–PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I (pp. 575-589). Cham: Springer International Publishing.

• DOI (open access): https://doi.org/10.1007/978-3-031-14714-2_40
• Author version (arXiv)

Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In Parallel Problem Solving from Nature–PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I (pp. 192-206). Cham: Springer International Publishing.

• DOI: https://doi.org/10.1007/978-3-031-14714-2_14
• Free access

Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). MOLE: Digging Tunnels Through Multimodal Multi-objective Landscapes. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 592-600).

• DOI: https://doi.org/10.1145/3512290.3528793
• Author version (arXiv)

2021

Schäpermeier, L., Grimme, C., & Kerschke, P. (2021). To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes. In Proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China.

• DOI: https://doi.org/10.1007/978-3-030-72062-9_50
• Author version (arXiv)

2020

Schäpermeier, L., Grimme, C., & Kerschke, P. (2020). One PLOT to Show Them All: Visualization of Efficient Sets in Multi-objective Landscapes. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 154–167.

• DOI: https://doi.org/10.1007/978-3-030-58115-2_11
• Author version (arXiv)