174 citations · 389 across the 26 of their papers we have counts for
29 papers
BONO-Bench: A Comprehensive Test Suite for Bi-objective Numerical Optimization with Traceable Pareto Sets
Lennart Schäpermeier, Pascal Kerschke
The evaluation of heuristic optimizers on test problems, better known as \emph{benchmarking}, is a cornerstone of research in multi-objective optimization. However, most test probl…
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX
Jonathan Heins, Darrell Whitley, Pascal Kerschke
The Edge Assembly Crossover (EAX) algorithm is the state-of-the-art heuristic for solving the Traveling Salesperson Problem (TSP). It regularly outperforms other methods, such as t…
Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem
Jonathan Heins, Lennart Schäpermeier, Pascal Kerschke +1
Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solv…
R2 v2: The Pareto-compliant R2 Indicator for Better Benchmarking in Bi-objective Optimization
Lennart Schäpermeier, Pascal Kerschke
In multi-objective optimization, set-based quality indicators are a cornerstone of benchmarking and performance assessment. They capture the quality of a set of trade-off solutions…
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
Konstantin Dietrich, Diederick Vermetten, Carola Doerr +1
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this g…
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems
Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann
In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize, in particular, single-objective continuous optimization problems h…
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
Vanessa Volz, Boris Naujoks, Pascal Kerschke +1
The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in ga…
Mixture of Decision Trees for Interpretable Machine Learning
Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke +1
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble archite…
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager +3
Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been p…
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Recent advances in the visualization of continuous multimodal multi-objective optimization (MMMOO) landscapes brought a new perspective to their search dynamics. Locally efficient…
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke +1
Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucia…
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Simultaneously visualizing the decision and objective space of continuous multi-objective optimization problems (MOPs) recently provided key contributions in understanding the stru…
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Vera Steinhoff, Pascal Kerschke, Pelin Aspar +2
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategie…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
Moritz Seiler, Janina Pohl, Jakob Bossek +2
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-i…
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
Vera Steinhoff, Pascal Kerschke, Christian Grimme
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization. Local optima are often preventing algorithms fr…
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Visualization techniques for the decision space of continuous multi-objective optimization problems (MOPs) are rather scarce in research. For long, all techniques focused on global…
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
Moritz Seiler, Heike Trautmann, Pascal Kerschke
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendou…
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
Jakob Bossek, Pascal Kerschke, Heike Trautmann
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respe…
Initial Design Strategies and their Effects on Sequential Model-Based Optimization
Jakob Bossek, Carola Doerr, Pascal Kerschke
Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design p…
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
Jakob Bossek, Katrin Casel, Pascal Kerschke +1
Several important optimization problems in the area of vehicle routing can be seen as a variant of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary co…
One-Shot Decision-Making with and without Surrogates
Jakob Bossek, Pascal Kerschke, Aneta Neumann +2
One-shot decision making is required in situations in which we can evaluate a fixed number of solution candidates but do not have any possibility for further, adaptive sampling. Su…
Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H. Hoos, Frank Neumann +1
It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is pa…
Estimation of component reliability from superposed renewal processes with masked cause of failure by means of latent variables
Agatha Rodrigues, Pascal Kerschke, Carlos Alberto de B. Pereira +4
In a system, there are identical replaceable components working for a given task and a failed component is replaced by a functioning one in the corresponding position, which charac…