Publication Indices
The citation numbers below are taken from Google Scholar (last updated: March 08, 2020).
- number of citations: 798
- h-index: 14
- i10-index: 20
For more recent citation numbers, please see my Google Scholar page.
Journal Articles
2020
| # | Publication |
|---|---|
| [J08] | Bossek, J., Kerschke, P. & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. In: Applied Soft Computing (ASOC), Vol. 88, Elsevier. [Link] [DOI] [BibTeX] |
2019
| # | Publication |
|---|---|
| [J07] | Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A. H., Trautmann, H. & Emmerich, M. T. M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. In: Evolutionary Computation (ECJ), Vol. 27(4), pp. 577 – 609, MIT Press. [Link] [DOI] [BibTeX] |
| [J06] | 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. In: Computational Statistics, pp. 977 – 991, Springer. [Link] [DOI] [BibTeX] |
| [J05] | Kerschke, P., Hoos, H. H., Neumann, F. & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. In: Evolutionary Computation (ECJ), Vol. 27(1), pp. 3 - 45, MIT Press. [Link] [DOI] [BibTeX] |
| [J04] | Kerschke, P. & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. In: Evolutionary Computation (ECJ), Vol. 27(1), pp. 99 - 127, MIT Press. [Link] [DOI] [BibTeX] |
2018
| # | Publication |
|---|---|
| [J03] | Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H. & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. In: Evolutionary Computation (ECJ), Vol. 26(4), pp. 597 – 620, MIT Press. [Link] [DOI] [BibTeX] |
2016
| # | Publication |
|---|---|
| [J02] | Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, T. 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. In: Artificial Intelligence (AIJ), Vol. 237, pp. 41 – 58, Elsevier. [Link] [DOI] [BibTeX] |
| [J01] | Liboschik, T., Kerschke, P., Fokianos, K. & Fried, R. (2016). Modelling Interventions in INGARCH processes. In: International Journal of Computer Mathematics, Vol. 93(4), pp. 640 – 657, Taylor & Francis. [Link] [DOI] [BibTeX] |
Conference Articles (Peer Reviewed)
2021
| # | Publication |
|---|---|
| [C29] | 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), Springer (in press). [BibTeX] |
| [C28] | Schaepermeier, 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), Springer (in press). [BibTeX] [arXiv] |
2020
| # | Publication |
|---|---|
| [C27] | 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 22nd Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1286 – 1294, ACM. [Link] [DOI] [BibTeX] [arXiv] |
| [C26] | 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 22nd Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 778 – 786, ACM. [Link] [DOI] [BibTeX] [arXiv] |
| [C25] | 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), pp. 111 - 124, Springer. [Link] [DOI] [BibTeX] |
| [C24] | Bossek, 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), pp. 1-8, IEEE. [Link] [DOI] [BibTeX] [arXiv] |
| [C23] | 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), pp. 996 – 1003, IEEE. [Link] [DOI] [BibTeX] |
| [C22] | Schaepermeier, 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), pp. 154-167, Springer. [Link] [DOI] [BibTeX] [arXiv] |
| [C21] | 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), 48 - 64 Springer. [Link] [DOI] [BibTeX] [arXiv] |
| [C20] | 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), pp. 1-8, IEEE. [Link] [DOI] [BibTeX] [arXiv] |
| [C19] | 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), pp. 2445 – 2452, IEEE. [Link] [DOI] [BibTeX] |
2019
| # | Publication |
|---|---|
| [C18] | 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), pp. 58 - 71, ACM. [Link] [DOI] [BibTeX] |
| [C17] | Doerr, C., Dreo, J. & Kerschke, P. (2019). Making a Case for (Hyper-)Parameter Tuning as Benchmark Problems. In: Proceedings of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1755 - 1764, ACM. [Link] [DOI] [BibTeX] |
| [C16] | 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), pp. 020052-1 - 020052-4, AIP Conference Proceedings. [Link] [DOI] [BibTeX] |
| [C15] | 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), pp. 126 - 138, Springer. [Link] [DOI] [BibTeX] |
| [C14] | Kerschke, P. & Preuss, M. (2019). Exploratory Landscape Analysis (Specialized Tutorial). In: Proceedings of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1137 - 1155, ACM. [Link] [DOI] [BibTeX] |
| [C13] | Rapin, J., Gallagher, M., Kerschke, P., Preuss, M. & Teytaud, O. (2019). Exploring the MLDA Benchmark on the Nevergrad Platform. In: Proceedings of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1888 - 1896, ACM. [Link] [DOI] [BibTeX] |
| [C12] | Volz, V., Naujoks, B., Kerschke, P. & Tusar, T. (2019). Single- and Multi-Objective Game-Benchmarkfor Evolutionary Algorithms. In: Proceedings of the 21st Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 647 - 655, ACM. [Link] [DOI] [BibTeX] |
2018
| # | Publication |
|---|---|
| [C11] | 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 20th Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1737 – 1744, ACM. [Link] [DOI] [BibTeX] |
2017
| # | Publication |
|---|---|
| [C10] | Hanster, C. & Kerschke, P. (2017). flaccogui: Exploratory Landscape Analysis for Everyone. In: Proceedings of the 19th Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1215 – 1222, ACM. [Link] [DOI] [BibTeX] |
| [C09] | 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), pp. 329 – 343, Springer. [Link] [DOI] [BibTeX] |
2016
| # | Publication |
|---|---|
| [C08] | Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A. H., Trautmann, H. & Emmerich, T. M. M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. In: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN), pp. 962 – 972, Springer. Best Paper Award [Link] [DOI] [BibTeX] |
| [C07] | Kerschke, P. & Trautmann, H. (2016). The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 5262 – 5269, IEEE. [Link] [DOI] [BibTeX] |
| [C06] | Kerschke, P., Preuss, M., Wessing, S. & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. In: Proceedings of the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 229 – 236, ACM. [Link] [DOI] [BibTeX] |
2015
| # | Publication |
|---|---|
| [C05] | Kerschke, P., Preuss, M., Wessing, S. & Trautmann, H. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. In: Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 265 – 272, ACM. [Link] [DOI] [BibTeX] |
| [C04] | 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 17th Annual Conference on Genetic and Evolutionary Computation (GECCO) Companion, pp. 1427 – 1428, ACM. [Link] [DOI] [BibTeX] [arXiv] |
| [C03] | Chinnov, A., Meske, C., Kerschke, P., 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), Association for Information Systems. [Link] [BibTeX] |
| [C02] | 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: Learning and Intelligent OptimizatioN 9 (LION), pp. 202 – 217, Springer. [Link] [DOI] [BibTeX] |
2014
| # | Publication |
|---|---|
| [C01] | 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: EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (EVOLVE 2014), pp. 115 – 131, Springer. [Link] [DOI] [BibTeX] |
Contributed Book Chapters
2019
| # | Publication |
|---|---|
| [B01] | Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco. In: Applications in Statistical Computing - From Music Data Analysis to Industrial Quality Improvement, pp. 93 - 123, Springer. [Link] [BibTeX] [arXiv] |
Technical Reports
2020
| # | Publication |
|---|---|
| [T01] | Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., López-Ibáñez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, W., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. arXiv:2007.03488. [Link] [BibTeX] [arXiv] |
PhD Thesis
- Kerschke, P. (2017). Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning. PhD Thesis at the Department of Information Systems, University of Münster, Germany.
[Link] [BibTeX]