174 citations · 389 across the 26 of their papers we have counts for
10 papers · 1 filter
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…