P. Kerschke

Most-cited papers

Automated Algorithm Selection: Survey and Perspectives
P. Kerschke, H. Hoos, Frank Neumann, H. Trautmann
2018Evolutionary Computation509 citations
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ASlib: A benchmark library for algorithm selection
B. Bischl, P. Kerschke, Lars Kotthoff, M. Lindauer +7 more
2015Artificial Intelligence251 citations Open access
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Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning
P. Kerschke, H. Trautmann
2017Evolutionary Computation187 citations Open access
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Benchmarking in Optimization: Best Practice and Open Issues
T. Bartz-Beielstein, C. Doerr, Jakob Bossek, Sowmya Chandrasekaran +11 more
2020arXiv.org158 citations
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Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco
P. Kerschke, H. Trautmann
2017Studies in Classification, Data Analysis, and Knowledge Organization125 citations Open access
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Detecting Funnel Structures by Means of Exploratory Landscape Analysis
P. Kerschke, M. Preuss, Simon Wessing, H. Trautmann
2015Annual Conference on Genetic and Evolutionary Computation99 citations
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Leveraging TSP Solver Complementarity through Machine Learning
P. Kerschke, Lars Kotthoff, Jakob Bossek, H. Hoos +1 more
2018Evolutionary Computation79 citations Open access
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Evolving diverse TSP instances by means of novel and creative mutation operators
Jakob Bossek, P. Kerschke, Aneta Neumann, Markus Wagner +2 more
2019Foundations of Genetic Algorithms77 citations
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Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection
Lars Kotthoff, P. Kerschke, H. Hoos, H. Trautmann
2015Learning and Intelligent Optimization73 citations
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OpenML: An R package to connect to the machine learning platform OpenML
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff +5 more
2017Computational statistics (Zeitschrift)64 citations Open access
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Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models
P. Kerschke, M. Preuss, Simon Wessing, H. Trautmann
2016Annual Conference on Genetic and Evolutionary Computation54 citations
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Cell Mapping Techniques for Exploratory Landscape Analysis
P. Kerschke, M. Preuss, C. Hernández, O. Schütze +5 more
201454 citations
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The R-Package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems
P. Kerschke, H. Trautmann
2016IEEE Congress on Evolutionary Computation44 citations
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An Expedition to Multimodal Multi-objective Optimization Landscapes
P. Kerschke, Christian Grimme
2017International Conference on Evolutionary Multi-Criterion Optimization42 citations
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Towards Analyzing Multimodality of Continuous Multiobjective Landscapes
P. Kerschke, Hao Wang, M. Preuss, Christian Grimme +3 more
2016Parallel Problem Solving from Nature39 citations
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Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization
Christian Grimme, P. Kerschke, Pelin Aspar, H. Trautmann +4 more
2021Computers & Operations Research37 citations Open access
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Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB
Jakob Bossek, C. Doerr, P. Kerschke
2020Annual Conference on Genetic and Evolutionary Computation36 citations Open access
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A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes
M. Seiler, Raphael Patrick Prager, P. Kerschke, H. Trautmann
2022Annual Conference on Genetic and Evolutionary Computation32 citations Open access
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Single- and multi-objective game-benchmark for evolutionary algorithms
Vanessa Volz, B. Naujoks, P. Kerschke, T. Tušar
2019Annual Conference on Genetic and Evolutionary Computation31 citations
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An Overview of Topic Discovery in Twitter Communication through Social Media Analytics
Andrey Chinnov, P. Kerschke, Christian Meske, Stefan Stieglitz +1 more
2015Americas Conference on Information Systems29 citations
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Search Dynamics on Multimodal Multiobjective Problems
P. Kerschke, Haibo Wang, M. Preuss, Christian Grimme +3 more
2019Evolutionary Computation28 citations Open access
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Modelling interventions in INGARCH processes
Tobias Liboschik, P. Kerschke, K. Fokianos, R. Fried
2016International Journal of Computational Mathematics28 citations Open access
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Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems
M. Seiler, P. Kerschke, H. Trautmann
2024Evolutionary Computation27 citations
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Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
M. Seiler, J. Pohl, Jakob Bossek, P. Kerschke +1 more
2020Parallel Problem Solving from Nature27 citations Open access
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One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
Lennart Schäpermeier, Christian Grimme, P. Kerschke
2020Parallel Problem Solving from Nature27 citations Open access
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Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods
Raphael Patrick Prager, M. Seiler, H. Trautmann, P. Kerschke
2022Parallel Problem Solving from Nature21 citations Open access
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Multimodality in Multi-objective Optimization - More Boon than Bane?
Christian Grimme, P. Kerschke, H. Trautmann
2019International Conference on Evolutionary Multi-Criterion Optimization20 citations
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A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms
Jakob Bossek, P. Kerschke, H. Trautmann
2020Applied Soft Computing19 citations
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A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, Jakob Bossek, J. Pohl, M. Seiler +2 more
2022Theoretical Computer Science18 citations
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HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Lennart Schneider, Lennart Schapermeier, Raphael Patrick Prager, B. Bischl +2 more
2022Parallel Problem Solving from Nature17 citations Open access
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Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers
P. Kerschke, Jakob Bossek, H. Trautmann
2018Annual Conference on Genetic and Evolutionary Computation17 citations
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To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
Lennart Schäpermeier, Christian Grimme, P. Kerschke
2020International Conference on Evolutionary Multi-Criterion Optimization16 citations Open access
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Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization
Raphael Patrick Prager, Moritz Vinzent Seiler, H. Trautmann, P. Kerschke
2021IEEE Symposium Series on Computational Intelligence15 citations
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Exploring the MLDA benchmark on the nevergrad platform
J. Rapin, M. Gallagher, P. Kerschke, M. Preuss +1 more
2019Annual Conference on Genetic and Evolutionary Computation15 citations
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The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics
Jakob Bossek, Katrin Casel, P. Kerschke, Frank Neumann
2020Annual Conference on Genetic and Evolutionary Computation14 citations
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flaccogui: exploratory landscape analysis for everyone
Christian Hanster, P. Kerschke
2017Annual Conference on Genetic and Evolutionary Computation14 citations
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Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
Konstantin Dietrich, D. Vermetten, C. Doerr, P. Kerschke
2024Annual Conference on Genetic and Evolutionary Computation12 citations Open access
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On the potential of normalized TSP features for automated algorithm selection
John P. Heins, Jakob Bossek, J. Pohl, M. Seiler +2 more
2021Foundations of Genetic Algorithms12 citations
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Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis
Raphael Patrick Prager, H. Trautmann, Hongya Wang, Thomas Bäck +1 more
2020IEEE Symposium Series on Computational Intelligence12 citations Open access
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Making a case for (Hyper-)parameter tuning as benchmark problems
C. Doerr, Johann Dréo, P. Kerschke
2019Annual Conference on Genetic and Evolutionary Computation12 citations Open access
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Sliding to the global optimum: How to benefit from non-global optima in multimodal multi-objective optimization
Christian Grimme, P. Kerschke, M. Emmerich, M. Preuss +2 more
201912 citations
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Towards Analyzing Multimodality of Multiobjective Landscapes
P. Kerschke, Hao Wang, M. Preuss, Christian Grimme +3 more
2016Parallel Problem Solving from Nature12 citations
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MOLE: digging tunnels through multimodal multi-objective landscapes
Lennart Schäpermeier, Christian Grimme, P. Kerschke
2022Annual Conference on Genetic and Evolutionary Computation11 citations Open access
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Finding ϵ-Locally Optimal Solutions for Multiobjective Multimodal Optimization
A. Rodríguez-Fernandez, Lennart Schäpermeier, Carlos Hernández, P. Kerschke +2 more
2025IEEE Transactions on Evolutionary Computation10 citations
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Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems
Lennart Schäpermeier, Christian Grimme, P. Kerschke
2022IEEE Transactions on Evolutionary Computation10 citations
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Evolving Sampling Strategies for One-Shot Optimization Tasks
Jakob Bossek, C. Doerr, P. Kerschke, Aneta Neumann +1 more
2020Parallel Problem Solving from Nature10 citations Open access
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Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features
Raphael Patrick Prager, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier +4 more
2023Foundations of Genetic Algorithms7 citations Open access
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Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
Vanessa Volz, B. Naujoks, P. Kerschke, T. Tušar
2023Applied Soft Computing7 citations Open access
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Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets
Lennart Schäpermeier, P. Kerschke, Christian Grimme, H. Trautmann
2023International Conference on Evolutionary Multi-Criterion Optimization7 citations
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Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Vera Steinhoff, P. Kerschke, Pelin Aspar, H. Trautmann +1 more
2020IEEE Symposium Series on Computational Intelligence7 citations Open access
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