• 2025

    Research article in proceedings (conference)

    Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P., Trautmann, H., & Schütze, O. (2025). Hot off the Press: Finding e-locally Optimal Solutions for Multi-objective Multimodal Optimization. In Ochoa, G. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 61–62). New York, NY, USA: Association for Computing Machinery, Inc.
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    Schäpermeier, L. (2025). Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems. In Filipic, B. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025 (pp. 1199–1207). online: ACM Press.
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  • 2024

    Research article in proceedings (conference)

    Heins, J., Schäpermeier, L., Kerschke, P., & Whitley, D. (2024). Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem. In Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tušar, T., Machado, P., & Bäck, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVIII (pp. 100–115). Cham: Springer Science and Business Media Deutschland GmbH.
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    Schäpermeier, L., & Kerschke, P. (2024). Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration. In Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tusar, T., Machado, P., & Bäck, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVIII — 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part IV (pp. 202–216). online: Springer Publishing.
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  • 2023

    Research article in proceedings (conference)

    Prager, P. R. &. D. K. &. S. L. &. S. L. &. B. B. &. K. P. &. T. H. &. M. O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In Chicano, F., Friedrich, T., Kötzing, T., & Rothlauf, F. (Eds.), FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 129–139). online: ACM Press.
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    Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023). Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In Li, K., & Wang, H. (Eds.), Proceedings of the International Conference Series on Evolutionary Multi-Criterion Optimization (pp. 291–304). Lecture Notes in Computer Science: Vol. 13970. Cham: Springer.
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  • 2022

    Research article in proceedings (conference)

    Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In Rudolph, G., Kononova, A., Aguirre, H., Kerschke, P., Ochoa, G., & Tu{š}ar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Cham: Springer International Publishing.
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    Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes. In Fieldsend, J. E. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 592–600). New York, NY, USA: Association for Computing Machinery, Inc.
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    Schneider, L., Schäpermeier, L., Prager, R., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO — ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). online: Springer Science and Business Media Deutschland GmbH.
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    Research article (journal)

    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, 22(2), 271–285.
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    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.
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  • 2021

    Research article in proceedings (conference)

    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, 632–644.
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  • 2020

    Research article in proceedings (conference)

    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.
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