1 citations · 1 across the 7 of their papers we have counts for
9 papers
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…
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…
Dynamic Bi-Objective Routing of Multiple Vehicles
Jakob Bossek, Christian Grimme, Heike Trautmann
In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours…
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
Jakob Bossek, Christian Grimme, Günter Rudolph +1
We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number…
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…