List of Publications

Publication Indices

The citation numbers below are taken from Google Scholar (last updated: December 2, 2019).

  • number of citations: 398
  • h-index: 12
  • i10-index: 15

citations

For more recent citation numbers, please see my Google Scholar page.

Journal Articles

2019

# Publication
[J08] 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]
[J07] Bossek, J., Kerschke, P. & Trautmann, H. (2019). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. In: Applied Soft Computing (ASOC), Elsevier (in 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)

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]

 

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]

 

R-Packages

Main Author

Contributor and/or Co-Author