---
_id: '48876'
abstract:
- lang: eng
  text: In recent years, Evolutionary Algorithms (EAs) have frequently been adopted
    to evolve instances for optimization problems that pose difficulties for one algorithm
    while being rather easy for a competitor and vice versa. Typically, this is achieved
    by either minimizing or maximizing the performance difference or ratio which serves
    as the fitness function. Repeating this process is useful to gain insights into
    strengths/weaknesses of certain algorithms or to build a set of instances with
    strong performance differences as a foundation for automatic per-instance algorithm
    selection or configuration. We contribute to this branch of research by proposing
    fitness-functions to evolve instances that show large performance differences
    for more than just two algorithms simultaneously. As a proof-of-principle, we
    evolve instances of the multi-component Traveling Thief Problem (TTP) for three
    incomplete TTP-solvers. Our results point out that our strategies are promising,
    but unsurprisingly their success strongly relies on the algorithms’ performance
    complementarity.
author:
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Markus
  full_name: Wagner, Markus
  last_name: Wagner
citation:
  ama: 'Bossek J, Wagner M. Generating Instances with Performance Differences for
    More than Just Two Algorithms. In: <i>Proceedings of the Genetic and Evolutionary
    Computation Conference Companion</i>. GECCO’21. Association for Computing Machinery;
    2021:1423–1432. doi:<a href="https://doi.org/10.1145/3449726.3463165">10.1145/3449726.3463165</a>'
  apa: Bossek, J., &#38; Wagner, M. (2021). Generating Instances with Performance
    Differences for More than Just Two Algorithms. <i>Proceedings of the Genetic and
    Evolutionary Computation Conference Companion</i>, 1423–1432. <a href="https://doi.org/10.1145/3449726.3463165">https://doi.org/10.1145/3449726.3463165</a>
  bibtex: '@inproceedings{Bossek_Wagner_2021, place={New York, NY, USA}, series={GECCO’21},
    title={Generating Instances with Performance Differences for More than Just Two
    Algorithms}, DOI={<a href="https://doi.org/10.1145/3449726.3463165">10.1145/3449726.3463165</a>},
    booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
    Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob
    and Wagner, Markus}, year={2021}, pages={1423–1432}, collection={GECCO’21} }'
  chicago: 'Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance
    Differences for More than Just Two Algorithms.” In <i>Proceedings of the Genetic
    and Evolutionary Computation Conference Companion</i>, 1423–1432. GECCO’21. New
    York, NY, USA: Association for Computing Machinery, 2021. <a href="https://doi.org/10.1145/3449726.3463165">https://doi.org/10.1145/3449726.3463165</a>.'
  ieee: 'J. Bossek and M. Wagner, “Generating Instances with Performance Differences
    for More than Just Two Algorithms,” in <i>Proceedings of the Genetic and Evolutionary
    Computation Conference Companion</i>, 2021, pp. 1423–1432, doi: <a href="https://doi.org/10.1145/3449726.3463165">10.1145/3449726.3463165</a>.'
  mla: Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences
    for More than Just Two Algorithms.” <i>Proceedings of the Genetic and Evolutionary
    Computation Conference Companion</i>, Association for Computing Machinery, 2021,
    pp. 1423–1432, doi:<a href="https://doi.org/10.1145/3449726.3463165">10.1145/3449726.3463165</a>.
  short: 'J. Bossek, M. Wagner, in: Proceedings of the Genetic and Evolutionary Computation
    Conference Companion, Association for Computing Machinery, New York, NY, USA,
    2021, pp. 1423–1432.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:41Z
department:
- _id: '819'
doi: 10.1145/3449726.3463165
extern: '1'
keyword:
- evolutionary algorithms
- evolving instances
- fitness function
- instance hardness
- traveling thief problem (TTP)
language:
- iso: eng
page: 1423–1432
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
  isbn:
  - 978-1-4503-8351-6
publisher: Association for Computing Machinery
series_title: GECCO’21
status: public
title: Generating Instances with Performance Differences for More than Just Two Algorithms
type: conference
user_id: '102979'
year: '2021'
...