• 2021

    Forschungsartikel in Sammelband (Konferenz)

    Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In Ishibuchi, H. e. a. (Ed.), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 311–322). Heidelberg, Berlin: Springer.
    Mehr Details BibTeX Gesamter Text DOI

    Prager, R. P., Moritz, V. H., & Pascal, (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA.
    Mehr Details BibTeX

    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.
    Mehr Details BibTeX Gesamter Text DOI

    Forschungsartikel (Zeitschrift)

    Rodrigues, A., Kerschke, P., de B., P. C. A., Trautmann, H., Wagner, C., Hellingrath, B., & Polpo, A. (2021). Estimation of component reliability from superposed renewal processes by means of latent variables. Computational Statistics, 2021.
    Mehr Details BibTeX DOI

  • 2020

    Forschungsartikel in Sammelband (Konferenz)

    Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico, 1286–1294.
    Mehr Details BibTeX Gesamter Text DOI

    Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies and their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico, 778–786.
    Mehr Details BibTeX Gesamter Text DOI

    Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 111–124.
    Mehr Details BibTeX DOI

    Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 1–8.
    Mehr Details BibTeX Gesamter Text

    Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 996–1003.
    Mehr Details BibTeX DOI

    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.
    Mehr Details BibTeX Gesamter Text DOI

    Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 48–64.
    Mehr Details BibTeX Gesamter Text DOI

    Seiler, M. V., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 1–8.
    Mehr Details BibTeX Gesamter Text DOI

    Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., & Grimme, C. (2020). Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2445–2452.
    Mehr Details BibTeX Gesamter Text DOI

    Forschungsartikel (Zeitschrift)

    Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 2020(88), 105901.
    Mehr Details BibTeX Gesamter Text DOI

    Sonstige wissenschaftliche Veröffentlichung

    Bartz-Beielstein, T., Doerr, C., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., López-Ibáñez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues.
    Mehr Details BibTeX Gesamter Text

    Steinhoff, V., Kerschke, P., & Grimme, C. (2020). Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems.
    Mehr Details BibTeX Gesamter Text

  • 2019

    Forschungsartikel (Buchbeitrag)

    Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., & Vichi, M. (Eds.), Applications in Statistical Computing (pp. 93–123). Springer.
    Mehr Details BibTeX DOI

    Forschungsartikel in Sammelband (Konferenz)

    Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In Proceedings of the 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 58–71.
    Mehr Details BibTeX DOI

    Doerr, C., Dreo, J., & Kerschke, P. (2019). Making a Case for (Hyper-)Parameter Tuning as Benchmark Problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1755–1764.
    Mehr Details BibTeX DOI

    Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., & Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. In Proceedings of the International Global Optimization Workshop (LeGO 2018), Leiden, The Netherlands, 020052-1-020052-4.
    Mehr Details BibTeX Gesamter Text DOI

    Grimme, C., Kerschke, P., & Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane?. In Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, MI, USA, 126–138.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., & Preuss, M. (2019). Exploratory Landscape Analysis. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1137–1155.
    Mehr Details BibTeX DOI

    Rapin, J., Gallagher, M., Kerschke, P., Preuss, M., & Teytaud, O. (2019). Exploring the MLDA Benchmark on the Nevergrad Platform. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1888–1896.
    Mehr Details BibTeX DOI

    Volz, V., Naujoks, B., Kerschke, P., & Tušar, T. (2019). Single- and Multi-Objective Game-Benchmarkfor Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), Prague, Czech Republic, 647–655.
    Mehr Details BibTeX DOI

    Forschungsartikel (Zeitschrift)

    Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 2019, 977–991.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ), 27(1), 3–45.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 27(4), 577–609.
    Mehr Details BibTeX Gesamter Text DOI

    Sonstige wissenschaftliche Veröffentlichung

    Bossek, J., Kerschke, P., Neumann, A., Neumann, F., & Doerr, C. (2019). One-Shot Decision-Making with and without Surrogates.
    Mehr Details BibTeX Gesamter Text

  • 2018

    Forschungsartikel in Sammelband (Konferenz)

    Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18) Companion, Kyoto, Japan, 1737–1744.
    Mehr Details BibTeX Gesamter Text DOI

    Pappa, G. L., Emmerich, M. T., Bazzan, A., Browne, W., Deb, K., Doerr, C., Ðurasević, M., Epitropakis, M. G., Haraldsson, S. O., Jakobovic, D., Kerschke, P., Krawiec, K., Lehre, P. K., Li, X., Lissovoi, A., Malo, P., Martí, L., Mei, Y., Merelo, J. J., Miller, J. F., Moraglio, A., Nebro, A. J., Nguyen, S., Ochoa, G., Oliveto, P., Picek, S., Pillay, N., Preuss, M., Schoenauer, M., Senkerik, R., Sinha, A., Shir, O., Sudholt, D., Whitley, D., Wineberg, M., Woodward, J., & Zhang, M. (2018). Tutorials at PPSN 2018. In Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV) (pp. 477–489). Cham: Springer International Publishing.
    Mehr Details BibTeX Gesamter Text DOI

    Purshouse, R., Zarges, C., Cussat-Blanc, S., Epitropakis, M. G., Gallagher, M., Jansen, T., Kerschke, P., Li, X., Lobo, F. G., Miller, J., Oliveto, P. S., Preuss, M., Squillero, G., Tonda, A., Wagner, M., Weise, T., Wilson, D., Wróbel, B., & Zamuda, A. (2018). Workshops at PPSN 2018. In Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV) (pp. 490–497). Cham: Springer International Publishing.
    Mehr Details BibTeX Gesamter Text DOI

    Forschungsartikel (Zeitschrift)

    Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620.
    Mehr Details BibTeX Gesamter Text DOI

  • 2017

    Forschungsartikel in Sammelband (Konferenz)

    Hanster, C., & Kerschke, P. (2017). flaccogui: Exploratory Landscape Analysis for Everyone. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 1215–1222.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., & Grimme, C. (2017). An Expedition to Multimodal Multi-Objective Optimization Landscapes. In Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Münster, Germany, 329–343.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., & Preuss, M. (2017). Exploratory Landscape Analysis: Advanced Tutorial at GECCO 2017. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 762–781.
    Mehr Details BibTeX Gesamter Text DOI

    Qualifikationsschrift (Dissertation, Habilitationsschrift)

    Kerschke, P. (2017). Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning. at the University of Münster.
    Mehr Details BibTeX Gesamter Text

    Sonstige wissenschaftliche Veröffentlichung

    Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2017). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P. (2017). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.
    Mehr Details BibTeX Gesamter Text

  • 2016

    Forschungsartikel in Sammelband (Konferenz)

    Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '16), Denver, CO, USA, 229–236.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., & Trautmann, H. (2016). The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Kanada.
    Mehr Details BibTeX Gesamter Text DOI

    Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, Scotland, 962–972.
    Mehr Details BibTeX Gesamter Text DOI

    Forschungsartikel (Zeitschrift)

    Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016). ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence Journal, 237, 41–58.
    Mehr Details BibTeX Gesamter Text DOI

    Liboschik, T., Kerschke, P., Fokianos, K., & Fried, R. (2016). Modelling interventions in INGARCH processes. International Journal of Computer Mathematics, 93(4), 640–657.
    Mehr Details BibTeX Gesamter Text DOI

  • 2015

    Forschungsartikel in Sammelband (Konferenz)

    Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., & Trautmann, H. (2015). An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. In Proceedings of the 20th Americas Conference on Information Systems (AMCIS '15), Puerto Rico, 1–10.
    Mehr Details BibTeX Gesamter Text

    Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain, 265–272.
    Mehr Details BibTeX Gesamter Text DOI

    Kotthoff, L., Kerschke, P., Hoos, H. H., & Trautmann, H. (2015). Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection. In Dhaenens, C., Jourdan, L., & Marmion, M.-E. (Eds.), Learning and Intelligent Optimization, 9th International Conference (pp. 202–217). Cham: Springer International Publishing.
    Mehr Details BibTeX Gesamter Text DOI

    Abstract in Online-Sammlung (Konferenz)

    Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms. Poster session presented at the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain.
    Mehr Details BibTeX Gesamter Text DOI

    Sonstige wissenschaftliche Veröffentlichung

    Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M. T., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2015). ASlib: A Benchmark Library for Algorithm Selection.
    Mehr Details BibTeX Gesamter Text

    Forschungsartikel in Online-Sammlung

    Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
    Mehr Details Gesamter Text DOI

  • 2014

    Forschungsartikel (Buchbeitrag)

    Kerschke, P., Preuss, M., Hernández, C., Schütze, O., Sun, J.-Q., Grimme, C., Rudolph, G., Bischl, B., & Trautmann, H. (2014). Cell Mapping Techniques for Exploratory Landscape Analysis. In Tantar, A.-A., Tantar, E., Sun, J.-Q., Zhang, W., Ding, Q., Schütze, O., Emmerich, M. T. M., Legrand, P., Del, M. P., & Coello, C. C. A. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (pp. 115–131). Advances in Intelligent Systems and Computing: Vol. 288. Cham: Springer International Publishing.
    Mehr Details BibTeX Gesamter Text DOI