Pascal Kerschke

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 78

Top Topics

Sampling Strategies

Optimisation Problems

Top Venues

GECCO (Companion)

Publications

Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems. CoRR (2024)
Lennart Schäpermeier, Pascal Kerschke
Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration. CoRR (2024)
Konstantin Dietrich, Diederick Vermetten, Carola Doerr, Pascal Kerschke
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. GECCO (2024)
Konstantin Dietrich, Diederick Vermetten, Carola Doerr, Pascal Kerschke
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. CoRR (2024)
Jonathan Heins, Lennart Schäpermeier, Pascal Kerschke, Darrell Whitley
Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem. CoRR (2024)
Pascal Kerschke, Mike Preuss
Exploratory Landscape Analysis. GECCO Companion (2023)
Pelin Aspar, Vera Steinhoff, Lennart Schäpermeier, Pascal Kerschke, Heike Trautmann, Christian Grimme
The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Nat. Comput. 22 (2) (2023)
Raphael Patrick Prager, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, Olaf Mersmann
Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. FOGA (2023)
Vanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tusar
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games. CoRR (2023)
Konstantin Dietrich, Pascal Kerschke
Evaluation of Algorithms from the Nevergrad Toolbox on the Strictly Box-Constrained SBOX-COST Benchmarking Suite. GECCO Companion (2023)
Anne Auger, Peter A. N. Bosman, Pascal Kerschke, Darrell Whitley, Lennart Schäpermeier
Challenges in Benchmarking Optimization Heuristics (Dagstuhl Seminar 23251). Dagstuhl Reports 13 (6) (2023)
Lennart Schäpermeier, Pascal Kerschke, Christian Grimme, Heike Trautmann
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. EMO (2023)
Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, Pascal Kerschke
A study on the effects of normalized TSP features for automated algorithm selection. Theor. Comput. Sci. 940 (Part) (2023)
Vanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tusar
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games. Appl. Soft Comput. 136 (2023)
Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke, Marco F. Huber
Mixture of Decision Trees for Interpretable Machine Learning. CoRR (2022)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
MOLE: digging tunnels through multimodal multi-objective landscapes. GECCO (2022)
Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke, Marco F. Huber
Mixture of Decision Trees for Interpretable Machine Learning. ICMLA (2022)
Raphael Patrick Prager, Moritz Vinzent Seiler, Heike Trautmann, Pascal Kerschke
Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. PPSN (1) (2022)
Jonathan Heins, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob Bossek, Heike Trautmann
BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. PPSN (1) (2022)
Agatha S. Rodrigues, Pascal Kerschke, Carlos Alberto De Bragança Pereira, Heike Trautmann, Carolin Wagner, Bernd Hellingrath, Adriano Polpo
Correction to: Estimation of component reliability from superposed renewal processes by means of latent variables. Comput. Stat. 37 (1) (2022)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems. IEEE Trans. Evol. Comput. 26 (6) (2022)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes. CoRR (2022)
Agatha S. Rodrigues, Pascal Kerschke, Carlos Alberto De Bragança Pereira, Heike Trautmann, Carolin Wagner, Bernd Hellingrath, Adriano Polpo
Estimation of component reliability from superposed renewal processes by means of latent variables. Comput. Stat. 37 (1) (2022)
Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke, Heike Trautmann
A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes. GECCO (2022)
Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke
HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. PPSN (1) (2022)
Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke, Heike Trautmann
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. CoRR (2022)
Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. CoRR (2022)
Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, Pascal Kerschke
On the potential of normalized TSP features for automated algorithm selection. FOGA (2021)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes. EMO (2021)
Pelin Aspar, Pascal Kerschke, Vera Steinhoff, Heike Trautmann, Christian Grimme
: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-objective Space by Means of Multiobjectivization. EMO (2021)
Christian Grimme, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike Preuss, André H. Deutz, Hao Wang, Michael Emmerich
Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Comput. Oper. Res. 136 (2021)
Raphael Patrick Prager, Moritz Vinzent Seiler, Heike Trautmann, Pascal Kerschke
Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. SSCI (2021)
Jakob Bossek, Pascal Kerschke, Heike Trautmann
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. CoRR (2020)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-objective Landscapes. PPSN (2) (2020)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes. CoRR (2020)
Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, Heike Trautmann
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. CoRR (2020)
Vera Steinhoff, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Christian Grimme
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. SSCI (2020)
Raphael Patrick Prager, Heike Trautmann, Hao Wang, Thomas Bäck, Pascal Kerschke
Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. SSCI (2020)
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes. CoRR (2020)
Moritz Seiler, Heike Trautmann, Pascal Kerschke
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries. CoRR (2020)
Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. CoRR (2020)
Jakob Bossek, Pascal Kerschke, Heike Trautmann
A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Appl. Soft Comput. 88 (2020)
Jakob Bossek, Carola Doerr, Pascal Kerschke
Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB. GECCO (2020)
Vera Steinhoff, Pascal Kerschke, Christian Grimme
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems. CoRR (2020)
Vera Steinhoff, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Christian Grimme
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. CoRR (2020)
Moritz Seiler, Heike Trautmann, Pascal Kerschke
Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. IJCNN (2020)
Jakob Bossek, Pascal Kerschke, Heike Trautmann
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. CEC (2020)
Jakob Bossek, Carola Doerr, Pascal Kerschke, Aneta Neumann, Frank Neumann
Evolving Sampling Strategies for One-Shot Optimization Tasks. PPSN (1) (2020)
Jakob Bossek, Carola Doerr, Pascal Kerschke
Initial Design Strategies and their Effects on Sequential Model-Based Optimization. CoRR (2020)
Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, Heike Trautmann
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. PPSN (1) (2020)
Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann
The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics. GECCO (2020)
Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise
Benchmarking in Optimization: Best Practice and Open Issues. CoRR (2020)
Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, Heike Trautmann
Evolving diverse TSP instances by means of novel and creative mutation operators. FOGA (2019)
Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz, Heike Trautmann, Michael T. M. Emmerich
Search Dynamics on Multimodal Multiobjective Problems. Evol. Comput. 27 (4) (2019)
Jérémy Rapin, Marcus Gallagher, Pascal Kerschke, Mike Preuss, Olivier Teytaud
Exploring the MLDA benchmark on the nevergrad platform. GECCO (Companion) (2019)
Vanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tusar
Single- and multi-objective game-benchmark for evolutionary algorithms. GECCO (2019)
Pascal Kerschke, Heike Trautmann
Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning. Evol. Comput. 27 (1) (2019)
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34 (3) (2019)
Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr
One-Shot Decision-Making with and without Surrogates. CoRR (2019)
Pascal Kerschke, Mike Preuss
Exploratory landscape analysis. GECCO (Companion) (2019)
Christian Grimme, Pascal Kerschke, Heike Trautmann
Multimodality in Multi-objective Optimization - More Boon than Bane? EMO (2019)
Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann
Automated Algorithm Selection: Survey and Perspectives. Evol. Comput. 27 (1) (2019)
Carola Doerr, Johann Dréo, Pascal Kerschke
Making a case for (Hyper-)parameter tuning as benchmark problems. GECCO (Companion) (2019)
Pascal Kerschke, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, Heike Trautmann
Leveraging TSP Solver Complementarity through Machine Learning. Evol. Comput. 26 (4) (2018)
Pascal Kerschke, Jakob Bossek, Heike Trautmann
Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers. GECCO (Companion) (2018)
Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann
Automated Algorithm Selection: Survey and Perspectives. CoRR (2018)
Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang
Tutorials at PPSN 2018. PPSN (2) (2018)
Robin C. Purshouse, Christine Zarges, Sylvain Cussat-Blanc, Michael G. Epitropakis, Marcus Gallagher, Thomas Jansen, Pascal Kerschke, Xiaodong Li, Fernando G. Lobo, Julian F. Miller, Pietro S. Oliveto, Mike Preuss, Giovanni Squillero, Alberto Paolo Tonda, Markus Wagner, Thomas Weise, Dennis Wilson, Borys Wróbel, Ales Zamuda
Workshops at PPSN 2018. PPSN (2) (2018)
Pascal Kerschke, Christian Grimme
An Expedition to Multimodal Multi-objective Optimization Landscapes. EMO (2017)
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR (2017)
Christian Hanster, Pascal Kerschke
flaccogui: exploratory landscape analysis for everyone. GECCO (Companion) (2017)
Pascal Kerschke, Mike Preuss
Exploratory landscape analysis: advanced tutorial at GECCO 2017. GECCO (Companion) (2017)
Pascal Kerschke, Heike Trautmann
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. CoRR (2017)
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237 (2016)
Pascal Kerschke, Mike Preuss, Simon Wessing, Heike Trautmann
Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. GECCO (2016)
Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz, Heike Trautmann, Michael Emmerich
Towards Analyzing Multimodality of Continuous Multiobjective Landscapes. PPSN (2016)
Pascal Kerschke, Heike Trautmann
The R-Package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems. CEC (2016)
Tobias Liboschik, Pascal Kerschke, Konstantinos Fokianos, Roland Fried
Modelling interventions in INGARCH processes. Int. J. Comput. Math. 93 (4) (2016)
Luis Martí, Christian Grimme, Pascal Kerschke, Heike Trautmann, Günter Rudolph
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms. CoRR (2015)
Lars Kotthoff, Pascal Kerschke, Holger H. Hoos, Heike Trautmann
Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection. LION (2015)
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
ASlib: A Benchmark Library for Algorithm Selection. CoRR (2015)
Luis Martí, Christian Grimme, Pascal Kerschke, Heike Trautmann, Günter Rudolph
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms. GECCO (Companion) (2015)
Pascal Kerschke, Mike Preuss, Simon Wessing, Heike Trautmann
Detecting Funnel Structures by Means of Exploratory Landscape Analysis. GECCO (2015)
Andrey Chinnov, Pascal Kerschke, Christian Meske, Stefan Stieglitz, Heike Trautmann
An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. AMCIS (2015)