---
_id: '48897'
abstract:
- lang: eng
  text: '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-instance algorithm selection (AS). We evolve instances with
    nodes where the solvers show strongly different performance profiles. These instances
    serve as a basis for an exploratory study on the identification of well-discriminating
    problem characteristics (features). Our results in a nutshell: we show that even
    though (1) promising features exist, (2) these are in line with previous results
    from the literature, and (3) models trained with these features are more accurate
    than models adopting sophisticated feature selection methods, the advantage is
    not close to the virtual best solver in terms of penalized average runtime and
    so is the performance gain over the single best solver. However, we show that
    a feature-free deep neural network based approach solely based on visual representation
    of the instances already matches classical AS model results and thus shows huge
    potential for future studies.'
author:
- first_name: Moritz
  full_name: Seiler, Moritz
  last_name: Seiler
- first_name: Janina
  full_name: Pohl, Janina
  last_name: Pohl
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
- first_name: Heike
  full_name: Trautmann, Heike
  last_name: Trautmann
citation:
  ama: 'Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive
    Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson
    Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag;
    2020:48–64. doi:<a href="https://doi.org/10.1007/978-3-030-58112-1_4">10.1007/978-3-030-58112-1_4</a>'
  apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020).
    Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection
    on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature}
    (PPSN XVI)</i>, 48–64. <a href="https://doi.org/10.1007/978-3-030-58112-1_4">https://doi.org/10.1007/978-3-030-58112-1_4</a>
  bibtex: '@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin,
    Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated
    Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href="https://doi.org/10.1007/978-3-030-58112-1_4">10.1007/978-3-030-58112-1_4</a>},
    booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag},
    author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal
    and Trautmann, Heike}, year={2020}, pages={48–64} }'
  chicago: 'Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike
    Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated
    Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem
    Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag,
    2020. <a href="https://doi.org/10.1007/978-3-030-58112-1_4">https://doi.org/10.1007/978-3-030-58112-1_4</a>.'
  ieee: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning
    as a Competitive Feature-Free Approach for Automated Algorithm Selection on the
    Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN
    XVI)</i>, 2020, pp. 48–64, doi: <a href="https://doi.org/10.1007/978-3-030-58112-1_4">10.1007/978-3-030-58112-1_4</a>.'
  mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach
    for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel
    Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64,
    doi:<a href="https://doi.org/10.1007/978-3-030-58112-1_4">10.1007/978-3-030-58112-1_4</a>.
  short: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem
    Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
    48–64.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:45Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_4
extern: '1'
keyword:
- Automated algorithm selection
- Deep learning
- Feature-based approaches
- Traveling Salesperson Problem
language:
- iso: eng
page: 48–64
place: Berlin, Heidelberg
publication: Parallel Problem Solving from {Nature} (PPSN XVI)
publication_identifier:
  isbn:
  - 978-3-030-58111-4
publisher: Springer-Verlag
status: public
title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm
  Selection on the Traveling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...