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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…
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…
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…
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…