• 2019

    Article in Journal

    Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ), 27(1), 3–45.
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    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.
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    Conference Paper

    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. (Accepted)
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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  • 2018

    Article in Journal

    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.
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    Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2018). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 0, 1–30. (In press)
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    Conference Paper

    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.
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    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.
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    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.
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  • 2017

    Article in Journal

    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 machine learning platform OpenML. Computational Statistics, 2017, 1–15.
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    Conference Paper

    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.
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    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.
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    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.
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    Kerschke, P., & Preuss, M. (2017). Exploratory Landscape Analysis: Advanced Tutorial at GECCO 2017. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18) Companion, Berlin, Germany, 762–781.
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    Thesis

    Kerschke, P. (2017). Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning. Dissertation at the University of Münster.
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    Other

    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.
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    Kerschke, P. (2017). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.
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  • 2016

    Article in Journal

    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.
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    Conference Paper

    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.
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    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.
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    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.
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  • 2015

    Conference Paper

    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.
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    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.
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    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.
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    Abstract / Poster

    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.
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    Other

    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.
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    Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
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  • 2014

    Article in Journal

    Liboschik, T., Kerschke, P., Fokianos, K., & Fried, R. (2014). Modelling interventions in INGARCH processes. International Journal of Computer Mathematics, 0(ja), 1–19.
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    Chapter in Book

    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.
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