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!

2024

Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P., Trautmann, H. & Schütze, O. (2024). Finding ε-locally Optimal Solutions for Multi-objective Multimodal Optimization. In IEEE Transactions on Evolutionary Computation.

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

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.

• 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)