Publications
Journal Publications
Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., Emmerich, M.
Peeking beyond peaks: Challenges and research potentials of continuous multimodal multiobjective optimization
Computers & Operations Research 2021(136), 105489
https://doi.org/10.1016/j.cor.2021.105489
Rodrigues, A., Kerschke, P., de B. Pereira, C. A., Trautmann, H., Wagner, C., Hellingrath, B., Polpo, A.
Estimation of component reliability from superposed renewal processes by means of latent variables
Computational Statistics 2021
https://doi.org/10.1007/s00180-021-01124-0
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.
https://doi.org/10.1016/j.asoc.2019.105901
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.
https://doi.org/10.1007/s00180-017-0742-2
Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019).
Automated Algorithm Selection: Survey and Perspectives.
Evolutionary Computation (ECJ), 27(1), 3–45.
https://doi.org/10.1162/evco_a_00242
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.
https://doi.org/10.1162/evco_a_00236
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.
https://doi.org/10.1162/evco_a_00234
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.
https://doi.org/10.1162/evco_a_00215
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.
https://doi.org/http://dx.doi.org/10.1016/j.artint.2016.04.003
Liboschik, T., Kerschke, P., Fokianos, K., & Fried, R. (2016).
Modelling interventions in INGARCH processes.
International Journal of Computer Mathematics, 93(4), 640–657.
https://doi.org/10.1080/00207160.2014.949250
Conference Publications
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: Proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China, 311 – 322. Springer.
https://doi.org/10.1007/978-3-030-72062-9_25
Heins, J., Bossek, J., Pohl, J., Moritz Seiler, Trautmann, H., Kerschke, P. (2021).
On the potential of normalized TSP features for automated algorithm selection
In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI), Dornbirn, Austria, 1 - 15, ACM.
https://doi.org/10.1145/3450218.3477308
* nominated for best paper award
Prager, R., Seiler, M., Trautmann, H., & Kerschke, P. (2021).
Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization
In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, IEEE.
(currently in press)
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, Springer.
https://doi.org/10.1007/978-3-030-72062-9_50
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, ACM.
https://doi.org/10.1145/3377930.3390243
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, ACM.
https://doi.org/10.1145/3377930.3390155
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, Springer.
https://doi.org/10.1007/978-3-030-58112-1_8
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, IEEE.
http://doi.org/10.1109/CEC48606.2020.9185613
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, IEEE.
https://doi.org/10.1109/SSCI47803.2020.9308510
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, Springer.
https://doi.org/10.1007/978-3-030-58115-2_11
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, Springer.
https://doi.org/10.1007/978-3-030-58112-1_4
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, IEEE.
https://doi.org/10.1109/IJCNN48605.2020.9207338
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, IEEE.
https://doi.org/10.1109/SSCI47803.2020.9308259
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, ACM.
https://doi.org/10.1145/3299904.3340307
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, ACM.
https://doi.org/10.1145/3319619.3326857
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.
https://doi.org/10.1063/1.5090019
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, Springer.
https://doi.org/10.1007/978-3-030-12598-1_11
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, ACM.
https://doi.org/10.1145/3319619.3323389
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, ACM.
https://doi.org/10.1145/3319619.3326830
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, ACM.
https://doi.org/10.1145/3321707.3321805
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, ACM.
https://doi.org/10.1145/3205651.3208233
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), 477–489, Springer.
https://doi.org/10.1007/978-3-319-99259-4_38
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), 490–497, Springer.
https://doi.org/10.1007/978-3-319-99259-4_39
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.
https://doi.org/10.1145/3067695.3082477
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.
https://doi.org/10.1007/978-3-319-54157-0_23
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.
https://doi.org/10.1145/3067695.3067696
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, ACM.
https://doi.org/doi.org/10.1145/2908812.2908845
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, Canada, IEEE.
https://doi.org/10.1109/CEC.2016.7748359
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, Springer.
https://doi.org/10.1007/978-3-319-45823-6_90
* Winner of the Best Paper Award
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.
http://aisel.aisnet.org/
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, ACM.
https://doi.org/10.1145/2739480.2754642
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.), Proceedings of the 9th International Conference on Learning and Intelligent Optimization (LION), 202–217, Springer.
https://doi.org/10.1007/978-3-319-19084-6_18
Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015).
Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15) Companion, Madrid, Spain, 1427–1428, ACM.
https://doi.org/10.1145/2739482.2764631
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 Coello, C. A. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, 115–131, Springer.
https://doi.org/10.1007/978-3-319-07494-8_9
Contributed Book Chapters
Kerschke, P., & Grimme, C. (2021).
Lifting the Multimodality-Fog in Continuous Multi-objective Optimization.
In: Preuss, M., Epitropakis, M. G., Li, X., & Fieldsend, J. (Eds.),
Metaheuristics for Finding Multiple Solutions, 89 – 111, Springer.
https://link.springer.com/chapter/10.1007/978-3-030-79553-5_4
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, 93–123. Springer.
https://doi.org/10.1007/978-3-030-25147-5_7
Other
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.
Preprint on arXiv.
https://arxiv.org/abs/2007.03488
Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.
Preprint on arXiv.
https://arxiv.org/abs/2005.13289
Steinhoff, V., Kerschke, P., & Grimme, C. (2020).
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems.
Preprint on arXiv.
https://arxiv.org/abs/2006.14423
Bossek, J., Kerschke, P., Neumann, A., Neumann, F., & Doerr, C. (2019).
One-Shot Decision-Making with and without Surrogates.
Preprint on arXiv.
https://arxiv.org/abs/1912.08956
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.
Preprint on arXiv.
https://arxiv.org/abs/1701.01293
Kerschke, P. (2017).
Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning.
Dissertation / PhD Thesis at the University of Münster.
http://nbn-resolving.de/
Kerschke, P. (2017).
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.
Preprint on arXiv.
https://arxiv.org/abs/1708.05258
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.
Preprint on arXiv.
https://arxiv.org/abs/1506.02465
Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015).
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
Preprint on arXiv.
https://arxiv.org/abs/1503.07845