Pascal Kerschke

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Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyVerified email at uni-paderborn.de
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.de
Christian GrimmeProfessor of Computational Social Science & Systems Analysis at University of Münster, GermanyVerified email at uni-muenster.de
Mike PreussUniversiteit LeidenVerified email at liacs.leidenuniv.nl
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Lennart SchäpermeierResearch Assistant, TU DresdenVerified email at tu-dresden.de
Markus WagnerAssociate Professor of Computer Science, Monash University, AustraliaVerified email at monash.edu
Michael T.M. Emmerich (Dr. rer. nat.)Lead AI Scientist Silo.ai & Jyväskylä University, Finland & LIACS Leiden Univ. NLVerified email at liacs.nl
Hao WangLeiden UniversityVerified email at liacs.leidenuniv.nl
Boris NaujoksCologne University of Applied SciencesVerified email at fh-koeln.de
Vanessa VolzCentrum Wiskunde & Informatica (CWI)Verified email at cwi.nl
Raphael Patrick PragerUniversity of MünsterVerified email at uni-muenster.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Aneta NeumannResearcher, The University of Adelaide, AustraliaVerified email at adelaide.edu.au
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Yuri MalitskyIBM ResearchVerified email at us.ibm.com

Pascal Kerschke

Professor of Big Data Analytics in Transportation, TU Dresden, Germany

Verified email at tu-dresden.de - Homepage

Automated Algorithm Selection Optimization Machine Learning Data Science Exploratory Landscape Analysis


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Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem J Heins, L Schäpermeier, P Kerschke, D Whitley arXiv preprint arXiv:2407.03927, 2024		2024
Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration L Schäpermeier, P Kerschke arXiv preprint arXiv:2407.01504, 2024		2024
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization K Dietrich, D Vermetten, C Doerr, P Kerschke arXiv preprint arXiv:2404.07539, 2024		2024
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems MV Seiler, P Kerschke, H Trautmann arXiv preprint arXiv:2401.01192, 2024	4	2024
Challenges in Benchmarking Optimization Heuristics (Dagstuhl Seminar 23251) A Auger, PAN Bosman, P Kerschke, D Whitley, L Schäpermeier Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2024		2024
Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features RP Prager, K Dietrich, L Schneider, L Schäpermeier, B Bischl, P Kerschke, ... Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023	2	2023
Evaluation of Algorithms from the Nevergrad Toolbox on the Strictly Box-Constrained SBOX-COST Benchmarking Suite K Dietrich, P Kerschke Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023	1	2023
Exploratory landscape analysis P Kerschke, M Preuss Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023	9	2023
The objective that freed me: a multi-objective local search approach for continuous single-objective optimization P Aspar, V Steinhoff, L Schäpermeier, P Kerschke, H Trautmann, ... Natural Computing 22 (2), 271-285, 2023		2023
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets L Schäpermeier, P Kerschke, C Grimme, H Trautmann International Conference on Evolutionary Multi-Criterion Optimization, 291-304, 2023	2	2023
Tools for landscape analysis of optimisation problems in procedural content generation for games V Volz, B Naujoks, P Kerschke, T Tušar Applied Soft Computing 136, 110121, 2023	5	2023
A study on the effects of normalized TSP features for automated algorithm selection J Heins, J Bossek, J Pohl, M Seiler, H Trautmann, P Kerschke Theoretical Computer Science 940, 123-145, 2023	11	2023
Mixture of Decision Trees for Interpretable Machine Learning S Brüggenjürgen, N Schaaf, P Kerschke, MF Huber 2022 21st IEEE International Conference on Machine Learning and Applications …, 2022		2022
Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems L Schäpermeier, C Grimme, P Kerschke IEEE Transactions on Evolutionary Computation 26 (6), 1306-1320, 2022	2	2022
Parallel Problem Solving from Nature–PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, T Tušar Springer Nature, 2022	1	2022
Automated algorithm selection in single-objective continuous optimization: A comparative study of deep learning and landscape analysis methods RP Prager, MV Seiler, H Trautmann, P Kerschke International Conference on Parallel Problem Solving from Nature, 3-17, 2022	17	2022
BBE: basin-based evaluation of multimodal multi-objective optimization problems J Heins, J Rook, L Schäpermeier, P Kerschke, J Bossek, H Trautmann International Conference on Parallel Problem Solving from Nature, 192-206, 2022	3	2022
HPO ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis L Schneider, L Schäpermeier, RP Prager, B Bischl, H Trautmann, ... International Conference on Parallel Problem Solving from Nature, 575-589, 2022	11	2022
MOLE: digging tunnels through multimodal multi-objective landscapes L Schäpermeier, C Grimme, P Kerschke Proceedings of the Genetic and Evolutionary Computation Conference, 592-600, 2022	6	2022
A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes MV Seiler, RP Prager, P Kerschke, H Trautmann Proceedings of the Genetic and Evolutionary Computation Conference, 657-665, 2022	18	2022

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