Pascal Kerschke

2.1k total citations
47 papers, 843 citations indexed

About

Pascal Kerschke is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Pascal Kerschke has authored 47 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 21 papers in Computational Theory and Mathematics and 7 papers in Computer Networks and Communications. Recurrent topics in Pascal Kerschke's work include Metaheuristic Optimization Algorithms Research (24 papers), Advanced Multi-Objective Optimization Algorithms (21 papers) and Evolutionary Algorithms and Applications (10 papers). Pascal Kerschke is often cited by papers focused on Metaheuristic Optimization Algorithms Research (24 papers), Advanced Multi-Objective Optimization Algorithms (21 papers) and Evolutionary Algorithms and Applications (10 papers). Pascal Kerschke collaborates with scholars based in Germany, Netherlands and Australia. Pascal Kerschke's co-authors include Heike Trautmann, Holger H. Hoos, Frank Neumann, Mike Preuß, Jakob Bossek, Simon Wessing, Lars Kotthoff, Joaquin Vanschoren, Bernd Bischl and Yuri Malitsky and has published in prestigious journals such as Artificial Intelligence, IEEE Transactions on Evolutionary Computation and Applied Soft Computing.

In The Last Decade

Pascal Kerschke

45 papers receiving 822 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pascal Kerschke Germany 13 601 376 129 117 98 47 843
Karl Bringmann Germany 14 439 0.7× 526 1.4× 31 0.2× 81 0.7× 86 0.9× 71 757
Prakash Shelokar India 10 577 1.0× 202 0.5× 97 0.8× 67 0.6× 33 0.3× 17 879
Á. E. Eiben Netherlands 9 392 0.7× 226 0.6× 75 0.6× 95 0.8× 45 0.5× 25 641
Nicos G. Pavlidis United Kingdom 13 661 1.1× 361 1.0× 31 0.2× 50 0.4× 108 1.1× 43 952
Khaled Mellouli Tunisia 15 511 0.9× 168 0.4× 46 0.4× 59 0.5× 252 2.6× 41 724
Kazuhisa Makino Japan 18 362 0.6× 587 1.6× 109 0.8× 329 2.8× 116 1.2× 146 1.1k
Yuelin Gao China 19 425 0.7× 402 1.1× 134 1.0× 72 0.6× 46 0.5× 110 1.0k
Jakub Mareček Czechia 10 241 0.4× 105 0.3× 126 1.0× 152 1.3× 156 1.6× 45 693
Pablo San Segundo Spain 14 220 0.4× 278 0.7× 121 0.9× 196 1.7× 67 0.7× 44 604

Countries citing papers authored by Pascal Kerschke

Since Specialization
Citations

This map shows the geographic impact of Pascal Kerschke's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pascal Kerschke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Kerschke more than expected).

Fields of papers citing papers by Pascal Kerschke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pascal Kerschke. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pascal Kerschke. The network helps show where Pascal Kerschke may publish in the future.

Co-authorship network of co-authors of Pascal Kerschke

This figure shows the co-authorship network connecting the top 25 collaborators of Pascal Kerschke. A scholar is included among the top collaborators of Pascal Kerschke based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pascal Kerschke. Pascal Kerschke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Whitley, Darrell, et al.. (2025). To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX. Proceedings of the Genetic and Evolutionary Computation Conference. 231–239.
2.
3.
Hernández, Carlos, et al.. (2024). Finding ϵ-Locally Optimal Solutions for Multiobjective Multimodal Optimization. IEEE Transactions on Evolutionary Computation. 29(5). 2019–2031. 4 indexed citations
4.
Vermetten, Diederick, et al.. (2024). Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 1007–1016. 2 indexed citations
5.
Volz, Vanessa, Boris Naujoks, Pascal Kerschke, & Tea Tušar. (2023). Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games. Applied Soft Computing. 136. 110121–110121. 3 indexed citations
6.
Bossek, Jakob, et al.. (2022). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science. 940. 123–145. 4 indexed citations
7.
Kerschke, Pascal, et al.. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing. 22(2). 271–285. 1 indexed citations
8.
Clever, Lena, et al.. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences. 12(18). 9094–9094. 3 indexed citations
9.
Grimme, Christian, et al.. (2022). MOLE. Proceedings of the Genetic and Evolutionary Computation Conference. 592–600. 3 indexed citations
10.
Bossek, Jakob, Pascal Kerschke, & Heike Trautmann. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Adelaide Research & Scholarship (AR&S) (University of Adelaide). arxiv 1603 8785. 1–8. 1 indexed citations
11.
Bossek, Jakob, et al.. (2020). The node weight dependent traveling salesperson problem. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1286–1294. 3 indexed citations
12.
Volz, Vanessa, Boris Naujoks, Pascal Kerschke, & Tea Tušar. (2019). Single- and multi-objective game-benchmark for evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 647–655. 14 indexed citations
13.
Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, et al.. (2019). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. Zurich Open Repository and Archive (University of Zurich).
14.
Grimme, Christian, Pascal Kerschke, Michael Emmerich, et al.. (2019). Sliding to the global optimum: How to benefit from non-global optima in multimodal multi-objective optimization. AIP conference proceedings. 2070. 20052–20052. 5 indexed citations
15.
Kerschke, Pascal, Jakob Bossek, & Heike Trautmann. (2018). Parameterization of state-of-the-art performance indicators. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1737–1744. 7 indexed citations
16.
Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, & Heike Trautmann. (2017). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation. 26(4). 597–620. 47 indexed citations
17.
Bischl, Bernd, Pascal Kerschke, Lars Kotthoff, et al.. (2016). ASlib: A benchmark library for algorithm selection. Artificial Intelligence. 237. 41–58. 115 indexed citations
18.
Kerschke, Pascal & Heike Trautmann. (2016). The R-Package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems. 5262–5269. 28 indexed citations
19.
Kerschke, Pascal, et al.. (2015). An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. Journal of the Association for Information Systems. 10 indexed citations
20.
Kerschke, Pascal, Mike Preuß, Simon Wessing, & Heike Trautmann. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. 265–272. 54 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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