---
_id: '21813'
author:
- first_name: Tim
full_name: Hansmeier, Tim
id: '49992'
last_name: Hansmeier
orcid: 0000-0003-1377-3339
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Hansmeier T, Platzner M. An Experimental Comparison of Explore/Exploit Strategies
for the Learning Classifier System XCS. In: GECCO ’21: Proceedings of the Genetic
and Evolutionary Computation Conference Companion. Association for Computing
Machinery (ACM); 2021:1639–1647. doi:10.1145/3449726.3463159'
apa: 'Hansmeier, T., & Platzner, M. (2021). An Experimental Comparison of Explore/Exploit
Strategies for the Learning Classifier System XCS. GECCO ’21: Proceedings of
the Genetic and Evolutionary Computation Conference Companion, 1639–1647.
https://doi.org/10.1145/3449726.3463159'
bibtex: '@inproceedings{Hansmeier_Platzner_2021, place={New York, NY, United States},
title={An Experimental Comparison of Explore/Exploit Strategies for the Learning
Classifier System XCS}, DOI={10.1145/3449726.3463159},
booktitle={GECCO ’21: Proceedings of the Genetic and Evolutionary Computation
Conference Companion}, publisher={Association for Computing Machinery (ACM)},
author={Hansmeier, Tim and Platzner, Marco}, year={2021}, pages={1639–1647} }'
chicago: 'Hansmeier, Tim, and Marco Platzner. “An Experimental Comparison of Explore/Exploit
Strategies for the Learning Classifier System XCS.” In GECCO ’21: Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 1639–1647.
New York, NY, United States: Association for Computing Machinery (ACM), 2021.
https://doi.org/10.1145/3449726.3463159.'
ieee: 'T. Hansmeier and M. Platzner, “An Experimental Comparison of Explore/Exploit
Strategies for the Learning Classifier System XCS,” in GECCO ’21: Proceedings
of the Genetic and Evolutionary Computation Conference Companion, Lille, France,
2021, pp. 1639–1647, doi: 10.1145/3449726.3463159.'
mla: 'Hansmeier, Tim, and Marco Platzner. “An Experimental Comparison of Explore/Exploit
Strategies for the Learning Classifier System XCS.” GECCO ’21: Proceedings
of the Genetic and Evolutionary Computation Conference Companion, Association
for Computing Machinery (ACM), 2021, pp. 1639–1647, doi:10.1145/3449726.3463159.'
short: 'T. Hansmeier, M. Platzner, in: GECCO ’21: Proceedings of the Genetic and
Evolutionary Computation Conference Companion, Association for Computing Machinery
(ACM), New York, NY, United States, 2021, pp. 1639–1647.'
conference:
end_date: 2021-07-14
location: Lille, France
name: International Workshop on Learning Classifier Systems (IWLCS 2021)
start_date: 2021-07-10
date_created: 2021-04-28T09:08:17Z
date_updated: 2022-09-02T09:42:38Z
department:
- _id: '78'
doi: 10.1145/3449726.3463159
language:
- iso: eng
page: 1639–1647
place: New York, NY, United States
project:
- _id: '4'
name: SFB 901 - Project Area C
- _id: '1'
name: SFB 901
- _id: '14'
name: SFB 901 - Subproject C2
publication: 'GECCO ''21: Proceedings of the Genetic and Evolutionary Computation
Conference Companion'
publication_identifier:
isbn:
- 978-1-4503-8351-6
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier
System XCS
type: conference
user_id: '49992'
year: '2021'
...
---
_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: Proceedings of the Genetic and Evolutionary
Computation Conference Companion. GECCO’21. Association for Computing Machinery;
2021:1423–1432. doi:10.1145/3449726.3463165'
apa: Bossek, J., & Wagner, M. (2021). Generating Instances with Performance
Differences for More than Just Two Algorithms. Proceedings of the Genetic and
Evolutionary Computation Conference Companion, 1423–1432. https://doi.org/10.1145/3449726.3463165
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={10.1145/3449726.3463165},
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 Proceedings of the Genetic
and Evolutionary Computation Conference Companion, 1423–1432. GECCO’21. New
York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449726.3463165.'
ieee: 'J. Bossek and M. Wagner, “Generating Instances with Performance Differences
for More than Just Two Algorithms,” in Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 2021, pp. 1423–1432, doi: 10.1145/3449726.3463165.'
mla: Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences
for More than Just Two Algorithms.” Proceedings of the Genetic and Evolutionary
Computation Conference Companion, Association for Computing Machinery, 2021,
pp. 1423–1432, doi:10.1145/3449726.3463165.
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'
...