---
_id: '46326'
abstract:
- lang: eng
text: Machine learning has become one of the most important tools in data analysis.
However, selecting the most appropriate machine learning algorithm and tuning
its hyperparameters to their optimal values remains a difficult task. This is
even more difficult for streaming applications where automated approaches are
often not available to help during algorithm selection and configuration. This
paper proposes the first approach for automated algorithm selection and configuration
of stream clustering algorithms. We train an ensemble of different stream clustering
algorithms and configurations in parallel and use the best performing configuration
to obtain a clustering solution. By drawing new configurations from better performing
ones, we are able to improve the ensemble performance over time. In large experiments
on real and artificial data we show how our ensemble approach can improve upon
default configurations and can also compete with a-posteriori algorithm configuration.
Our approach is considerably faster than a-posteriori approaches and applicable
in real-time. In addition, it is not limited to stream clustering and can be generalised
to all streaming applications, including stream classification and regression.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Albert
full_name: Bifet, Albert
last_name: Bifet
- first_name: Bernhard
full_name: Pfahringer, Bernhard
last_name: Pfahringer
citation:
ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm
Selection and Configuration of Stream Clustering Algorithms. In: Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020).
; 2020:80–95. doi:10.1007/978-3-030-53552-0_10'
apa: 'Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). confStream:
Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.
Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020), 80–95. https://doi.org/10.1007/978-3-030-53552-0_10'
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens,
Greece}, title={confStream: Automated Algorithm Selection and Configuration of
Stream Clustering Algorithms}, DOI={10.1007/978-3-030-53552-0_10},
booktitle={Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert
and Pfahringer, Bernhard}, year={2020}, pages={80–95} }'
chicago: 'Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms.” In Proceedings of the 14$^th$ Learning and Intelligent Optimization
Conference (LION 2020), 80–95. Athens, Greece, 2020. https://doi.org/10.1007/978-3-030-53552-0_10.'
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated
Algorithm Selection and Configuration of Stream Clustering Algorithms,” in Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020),
2020, pp. 80–95, doi: 10.1007/978-3-030-53552-0_10.'
mla: 'Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration
of Stream Clustering Algorithms.” Proceedings of the 14$^th$ Learning and Intelligent
Optimization Conference (LION 2020), 2020, pp. 80–95, doi:10.1007/978-3-030-53552-0_10.'
short: 'M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the
14$^th$ Learning and Intelligent Optimization Conference (LION 2020), Athens,
Greece, 2020, pp. 80–95.'
date_created: 2023-08-04T07:36:03Z
date_updated: 2023-10-16T13:03:36Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-53552-0_10
language:
- iso: eng
page: 80–95
place: Athens, Greece
publication: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020)
status: public
title: 'confStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46327'
abstract:
- lang: eng
text: In online media environments, nostalgia can be used as important ingredient
of propaganda strategies, specifically, by creating societal pessimism. This work
addresses the automated detection of nostalgic text as a first step towards automatically
identifying nostalgia-based manipulation strategies. We compare the performance
of standard machine learning approaches on this challenge and demonstrate the
successful transfer of the best performing approach to real-world nostalgia detection
in a case study.
author:
- first_name: Clever
full_name: Lena, Clever
last_name: Lena
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Lena C, Frischlich L, Trautmann H, Grimme C. Automated detection of nostalgic
text in the context of societal pessimism. In: Grimme C, Preuß M, Takes F, Waldherr
A, eds. Disinformation in Open Online Media. ; 2020:48–58.'
apa: Lena, C., Frischlich, L., Trautmann, H., & Grimme, C. (2020). Automated
detection of nostalgic text in the context of societal pessimism. In C. Grimme,
M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in open online
media (pp. 48–58).
bibtex: '@inproceedings{Lena_Frischlich_Trautmann_Grimme_2020, place={Hamburg, Deutschland},
title={Automated detection of nostalgic text in the context of societal pessimism},
booktitle={Disinformation in open online media}, author={Lena, Clever and Frischlich,
Lena and Trautmann, Heike and Grimme, Christian}, editor={Grimme, Christian and
Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={48–58}
}'
chicago: Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated
Detection of Nostalgic Text in the Context of Societal Pessimism.” In Disinformation
in Open Online Media, edited by Christian Grimme, Mike Preuß, Frank Takes,
and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020.
ieee: C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection
of nostalgic text in the context of societal pessimism,” in Disinformation
in open online media, 2020, pp. 48–58.
mla: Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context
of Societal Pessimism.” Disinformation in Open Online Media, edited by
Christian Grimme et al., 2020, pp. 48–58.
short: 'C. Lena, L. Frischlich, H. Trautmann, C. Grimme, in: C. Grimme, M. Preuß,
F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Hamburg, Deutschland,
2020, pp. 48–58.'
date_created: 2023-08-04T07:36:43Z
date_updated: 2023-10-16T13:03:56Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
- first_name: Frank
full_name: Takes, Frank
last_name: Takes
- first_name: Annie
full_name: Waldherr, Annie
last_name: Waldherr
language:
- iso: eng
page: 48–58
place: Hamburg, Deutschland
publication: Disinformation in open online media
status: public
title: Automated detection of nostalgic text in the context of societal pessimism
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46329'
abstract:
- lang: eng
text: The past decade has been characterized by a strong increase in the use of
social media and a continuous growth of public online discussion. With the failure
of purely manual moderation, platform operators started searching for semi-automated
solutions, where the application of Natural Language Processing (NLP) and Machine
Learning (ML) techniques is promising. However, this requires huge financial investments
for algorithmic implementations, data collection, and model training, which only
big players can afford. To support smaller or medium-sized media enterprises (SME),
we developed an integrated comment moderation system as an IT platform. This platform
acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating
such a platform, however, requires a robust technology stack, integrated workflows
and well-defined interfaces between all parties. In this paper, we develop and
discuss a suitable IT architecture and present a prototypical implementation.
author:
- first_name: Dennis M.
full_name: Riehle, Dennis M.
last_name: Riehle
- first_name: Marco
full_name: Niemann, Marco
last_name: Niemann
- first_name: Jens
full_name: Brunk, Jens
last_name: Brunk
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
citation:
ama: 'Riehle DM, Niemann M, Brunk J, Assenmacher D, Trautmann H, Becker J. Building
an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool. In: Meiselwitz G, ed. Social Computing and Social Media. Participation,
User Experience, Consumer Experience, and Applications of Social Computing.
Springer International Publishing; 2020:71–86.'
apa: Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., &
Becker, J. (2020). Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), Social Computing
and Social Media. Participation, User Experience, Consumer Experience, and Applications
of Social Computing (pp. 71–86). Springer International Publishing.
bibtex: '@inproceedings{Riehle_Niemann_Brunk_Assenmacher_Trautmann_Becker_2020,
place={Cham}, title={Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool}, booktitle={Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing}, publisher={Springer International Publishing}, author={Riehle, Dennis
M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike
and Becker, Jörg}, editor={Meiselwitz, Gabriele}, year={2020}, pages={71–86} }'
chicago: 'Riehle, Dennis M., Marco Niemann, Jens Brunk, Dennis Assenmacher, Heike
Trautmann, and Jörg Becker. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” In Social Computing and Social
Media. Participation, User Experience, Consumer Experience, and Applications of
Social Computing, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International
Publishing, 2020.'
ieee: D. M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, and J. Becker,
“Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool,” in Social Computing and Social Media. Participation, User Experience,
Consumer Experience, and Applications of Social Computing, 2020, pp. 71–86.
mla: Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing, edited by Gabriele Meiselwitz, Springer International Publishing,
2020, pp. 71–86.
short: 'D.M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, J. Becker,
in: G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User
Experience, Consumer Experience, and Applications of Social Computing, Springer
International Publishing, Cham, 2020, pp. 71–86.'
date_created: 2023-08-04T07:38:42Z
date_updated: 2023-10-16T13:04:36Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Gabriele
full_name: Meiselwitz, Gabriele
last_name: Meiselwitz
language:
- iso: eng
page: 71–86
place: Cham
publication: Social Computing and Social Media. Participation, User Experience, Consumer
Experience, and Applications of Social Computing
publication_identifier:
isbn:
- 978-3-030-49576-3
publisher: Springer International Publishing
status: public
title: Building an Integrated Comment Moderation System – Towards a Semi-automatic
Moderation Tool
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46333'
abstract:
- lang: eng
text: ' Recently, social bots, (semi-) automatized accounts in social media, gained
global attention in the context of public opinion manipulation. Dystopian scenarios
like the malicious amplification of topics, the spreading of disinformation, and
the manipulation of elections through “opinion machines” created headlines around
the globe. As a consequence, much research effort has been put into the classification
and detection of social bots. Yet, it is still unclear how easy an average online
media user can purchase social bots, which platforms they target, where they originate
from, and how sophisticated these bots are. This work provides a much needed new
perspective on these questions. By providing insights into the markets of social
bots in the clearnet and darknet as well as an exhaustive analysis of freely available
software tools for automation during the last decade, we shed light on the availability
and capabilities of automated profiles in social media platforms. Our results
confirm the increasing importance of social bot technology but also uncover an
as yet unknown discrepancy of theoretical and practically achieved artificial
intelligence in social bots: while literature reports on a high degree of intelligence
for chat bots and assumes the same for social bots, the observed degree of intelligence
in social bot implementations is limited. In fact, the overwhelming majority of
available services and software are of supportive nature and merely provide modules
of automation instead of fully fledged “intelligent” social bots. '
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Thorsten
full_name: Quandt, Thorsten
last_name: Quandt
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors. Social Media
+ Society. 2020;6(3):2056305120939264. doi:10.1177/2056305120939264'
apa: 'Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., &
Grimme, C. (2020). Demystifying Social Bots: On the Intelligence of Automated
Social Media Actors. Social Media + Society, 6(3), 2056305120939264.
https://doi.org/10.1177/2056305120939264'
bibtex: '@article{Assenmacher_Clever_Frischlich_Quandt_Trautmann_Grimme_2020, title={Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors}, volume={6},
DOI={10.1177/2056305120939264},
number={3}, journal={Social Media + Society}, author={Assenmacher, Dennis and
Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and
Grimme, Christian}, year={2020}, pages={2056305120939264} }'
chicago: 'Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike
Trautmann, and Christian Grimme. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society 6, no. 3 (2020):
2056305120939264. https://doi.org/10.1177/2056305120939264.'
ieee: 'D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C.
Grimme, “Demystifying Social Bots: On the Intelligence of Automated Social Media
Actors,” Social Media + Society, vol. 6, no. 3, p. 2056305120939264, 2020,
doi: 10.1177/2056305120939264.'
mla: 'Assenmacher, Dennis, et al. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society, vol. 6, no.
3, 2020, p. 2056305120939264, doi:10.1177/2056305120939264.'
short: D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, C. Grimme,
Social Media + Society 6 (2020) 2056305120939264.
date_created: 2023-08-04T07:41:37Z
date_updated: 2023-10-16T13:06:34Z
department:
- _id: '34'
- _id: '819'
doi: 10.1177/2056305120939264
intvolume: ' 6'
issue: '3'
language:
- iso: eng
page: '2056305120939264'
publication: Social Media + Society
status: public
title: 'Demystifying Social Bots: On the Intelligence of Automated Social Media Actors'
type: journal_article
user_id: '15504'
volume: 6
year: '2020'
...
---
_id: '46332'
abstract:
- lang: eng
text: 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 strategies that can get stuck. It also hinders meta-heuristics
like evolutionary algorithms in convergence to the global optimum. In this paper
we present a new concept of gradient descent, which is able to escape local traps.
It relies on multiobjectivization of the original problem and applies the recently
proposed and here slightly modified multi-objective local search mechanism MOGSA.
We use a sophisticated visualization technique for multi-objective problems to
prove the working principle of our idea. As such, this work highlights the transfer
of new insights from the multi-objective to the single-objective domain and provides
first visual evidence that multiobjectivization can link single-objective local
optima in multimodal landscapes.
author:
- first_name: Vera
full_name: Steinhoff, Vera
last_name: Steinhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Pelin
full_name: Aspar, Pelin
last_name: Aspar
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Steinhoff V, Kerschke P, Aspar P, Trautmann H, Grimme C. Multiobjectivization
of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient
Descent. In: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI). ; 2020:2445–2452. doi:10.1109/SSCI47803.2020.9308259'
apa: 'Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., & Grimme, C. (2020).
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From
Multi-Objective Gradient Descent. Proceedings of the IEEE Symposium Series
on Computational Intelligence (SSCI), 2445–2452. https://doi.org/10.1109/SSCI47803.2020.9308259'
bibtex: '@inproceedings{Steinhoff_Kerschke_Aspar_Trautmann_Grimme_2020, place={Canberra,
Australia}, title={Multiobjectivization of Local Search: Single-Objective Optimization
Benefits From Multi-Objective Gradient Descent}, DOI={10.1109/SSCI47803.2020.9308259},
booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)}, author={Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann,
Heike and Grimme, Christian}, year={2020}, pages={2445–2452} }'
chicago: 'Steinhoff, Vera, Pascal Kerschke, Pelin Aspar, Heike Trautmann, and Christian
Grimme. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits
From Multi-Objective Gradient Descent.” In Proceedings of the IEEE Symposium
Series on Computational Intelligence (SSCI), 2445–2452. Canberra, Australia,
2020. https://doi.org/10.1109/SSCI47803.2020.9308259.'
ieee: 'V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, and C. Grimme, “Multiobjectivization
of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient
Descent,” in Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI), 2020, pp. 2445–2452, doi: 10.1109/SSCI47803.2020.9308259.'
mla: 'Steinhoff, Vera, et al. “Multiobjectivization of Local Search: Single-Objective
Optimization Benefits From Multi-Objective Gradient Descent.” Proceedings of
the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp.
2445–2452, doi:10.1109/SSCI47803.2020.9308259.'
short: 'V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, C. Grimme, in: Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia,
2020, pp. 2445–2452.'
date_created: 2023-08-04T07:40:33Z
date_updated: 2023-10-16T13:05:49Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/SSCI47803.2020.9308259
language:
- iso: eng
page: 2445–2452
place: Canberra, Australia
publication: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)
status: public
title: 'Multiobjectivization of Local Search: Single-Objective Optimization Benefits
From Multi-Objective Gradient Descent'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '48301'
author:
- first_name: Max
full_name: Glockner, Max
last_name: Glockner
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
citation:
ama: 'Glockner M, Habernal I, Gurevych I. Why do you think that? Exploring Faithful
Sentence-Level Rationales Without Supervision. In: Findings of the Association
for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics;
2020. doi:10.18653/v1/2020.findings-emnlp.97'
apa: 'Glockner, M., Habernal, I., & Gurevych, I. (2020). Why do you think that?
Exploring Faithful Sentence-Level Rationales Without Supervision. Findings
of the Association for Computational Linguistics: EMNLP 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.97'
bibtex: '@inproceedings{Glockner_Habernal_Gurevych_2020, title={Why do you think
that? Exploring Faithful Sentence-Level Rationales Without Supervision}, DOI={10.18653/v1/2020.findings-emnlp.97},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020},
publisher={Association for Computational Linguistics}, author={Glockner, Max and
Habernal, Ivan and Gurevych, Iryna}, year={2020} }'
chicago: 'Glockner, Max, Ivan Habernal, and Iryna Gurevych. “Why Do You Think That?
Exploring Faithful Sentence-Level Rationales Without Supervision.” In Findings
of the Association for Computational Linguistics: EMNLP 2020. Association
for Computational Linguistics, 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.97.'
ieee: 'M. Glockner, I. Habernal, and I. Gurevych, “Why do you think that? Exploring
Faithful Sentence-Level Rationales Without Supervision,” 2020, doi: 10.18653/v1/2020.findings-emnlp.97.'
mla: 'Glockner, Max, et al. “Why Do You Think That? Exploring Faithful Sentence-Level
Rationales Without Supervision.” Findings of the Association for Computational
Linguistics: EMNLP 2020, Association for Computational Linguistics, 2020,
doi:10.18653/v1/2020.findings-emnlp.97.'
short: 'M. Glockner, I. Habernal, I. Gurevych, in: Findings of the Association for
Computational Linguistics: EMNLP 2020, Association for Computational Linguistics,
2020.'
date_created: 2023-10-19T08:29:15Z
date_updated: 2023-10-19T12:10:18Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2020.findings-emnlp.97
language:
- iso: eng
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2020'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Why do you think that? Exploring Faithful Sentence-Level Rationales Without
Supervision
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '16790'
author:
- first_name: Sarah Claudia
full_name: Krings, Sarah Claudia
id: '64063'
last_name: Krings
orcid: 0000-0001-8040-7553
- first_name: Enes
full_name: Yigitbas, Enes
id: '8447'
last_name: Yigitbas
orcid: 0000-0002-5967-833X
- first_name: Ivan
full_name: Jovanovikj, Ivan
id: '39187'
last_name: Jovanovikj
orcid: https://orcid.org/0000-0002-1838-794X
- first_name: Stefan
full_name: Sauer, Stefan
id: '447'
last_name: Sauer
orcid: 0000-0003-3084-0409
- first_name: Gregor
full_name: Engels, Gregor
id: '107'
last_name: Engels
citation:
ama: 'Krings SC, Yigitbas E, Jovanovikj I, Sauer S, Engels G. Development Framework
for Context-Aware Augmented Reality Applications. In: Proceedings of the 12th
ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020).
; 2020. doi:10.1145/3393672.3398640'
apa: Krings, S. C., Yigitbas, E., Jovanovikj, I., Sauer, S., & Engels, G. (2020).
Development Framework for Context-Aware Augmented Reality Applications. Proceedings
of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
(EICS 2020). https://doi.org/10.1145/3393672.3398640
bibtex: '@inproceedings{Krings_Yigitbas_Jovanovikj_Sauer_Engels_2020, title={Development
Framework for Context-Aware Augmented Reality Applications}, DOI={10.1145/3393672.3398640},
booktitle={Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020)}, author={Krings, Sarah Claudia and Yigitbas, Enes
and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}, year={2020} }'
chicago: Krings, Sarah Claudia, Enes Yigitbas, Ivan Jovanovikj, Stefan Sauer, and
Gregor Engels. “Development Framework for Context-Aware Augmented Reality Applications.”
In Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020), 2020. https://doi.org/10.1145/3393672.3398640.
ieee: 'S. C. Krings, E. Yigitbas, I. Jovanovikj, S. Sauer, and G. Engels, “Development
Framework for Context-Aware Augmented Reality Applications,” 2020, doi: 10.1145/3393672.3398640.'
mla: Krings, Sarah Claudia, et al. “Development Framework for Context-Aware Augmented
Reality Applications.” Proceedings of the 12th ACM SIGCHI Symposium on Engineering
Interactive Computing Systems (EICS 2020), 2020, doi:10.1145/3393672.3398640.
short: 'S.C. Krings, E. Yigitbas, I. Jovanovikj, S. Sauer, G. Engels, in: Proceedings
of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
(EICS 2020), 2020.'
date_created: 2020-04-21T11:49:52Z
date_updated: 2023-12-07T10:42:15Z
department:
- _id: '66'
- _id: '534'
doi: 10.1145/3393672.3398640
language:
- iso: eng
publication: Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020)
publication_identifier:
isbn:
- 978-1-4503-7984-7/20/06
status: public
title: Development Framework for Context-Aware Augmented Reality Applications
type: conference
user_id: '8447'
year: '2020'
...
---
_id: '48847'
abstract:
- lang: eng
text: Dynamic optimization problems have gained significant attention in evolutionary
computation as evolutionary algorithms (EAs) can easily adapt to changing environments.
We show that EAs can solve the graph coloring problem for bipartite graphs more
efficiently by using dynamic optimization. In our approach the graph instance
is given incrementally such that the EA can reoptimize its coloring when a new
edge introduces a conflict. We show that, when edges are inserted in a way that
preserves graph connectivity, Randomized Local Search (RLS) efficiently finds
a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS
and other EAs need exponential expected time in a static optimization scenario.
We investigate different ways of building up the graph by popular graph traversals
such as breadth-first-search and depth-first-search and analyse the resulting
runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$})
RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using
3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite
graph, outperforming offspring populations. This is the first example where an
island model guarantees a speedup that is not bounded in the number of islands.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics
for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’20. Association for Computing
Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.
Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.
https://doi.org/10.1145/3377930.3390174
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA},
series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization}, DOI={10.1145/3377930.3390174},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery,
2020. https://doi.org/10.1145/3377930.3390174.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized
Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285,
doi: 10.1145/3377930.3390174.'
mla: Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285,
doi:10.1145/3377930.3390174.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1277–1285.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:41Z
department:
- _id: '819'
doi: 10.1145/3377930.3390174
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1277–1285
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: More Effective Randomized Search Heuristics for Graph Coloring through Dynamic
Optimization
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48849'
abstract:
- lang: eng
text: One-shot optimization tasks require to determine the set of solution candidates
prior to their evaluation, i.e., without possibility for adaptive sampling. We
consider two variants, classic one-shot optimization (where our aim is to find
at least one solution of high quality) and one-shot regression (where the goal
is to fit a model that resembles the true problem as well as possible). For both
tasks it seems intuitive that well-distributed samples should perform better than
uniform or grid-based samples, since they show a better coverage of the decision
space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy
point sets are indeed very commonly used designs for one-shot optimization tasks.
We study in this work how well low star discrepancy correlates with performance
in one-shot optimization. Our results confirm an advantage of low-discrepancy
designs, but also indicate the correlation between discrepancy values and overall
performance is rather weak. We then demonstrate that commonly used designs may
be far from optimal. More precisely, we evolve 24 very specific designs that each
achieve good performance on one of our benchmark problems. Interestingly, we find
that these specifically designed samples yield surprisingly good performance across
the whole benchmark set. Our results therefore give strong indication that significant
performance gains over state-of-the-art one-shot sampling techniques are possible,
and that evolutionary algorithms can be an efficient means to evolve these.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies
for One-Shot Optimization Tasks. In: Parallel Problem Solving from Nature (PPSN
XVI). Springer-Verlag; 2020:111–124. doi:10.1007/978-3-030-58112-1_8'
apa: Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020).
Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem
Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin,
Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks},
DOI={10.1007/978-3-030-58112-1_8},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta
and Neumann, Frank}, year={2020}, pages={111–124} }'
chicago: 'Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank
Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In Parallel
Problem Solving from Nature (PPSN XVI), 111–124. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_8.'
ieee: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling
Strategies for One-Shot Optimization Tasks,” in Parallel Problem Solving from
Nature (PPSN XVI), 2020, pp. 111–124, doi: 10.1007/978-3-030-58112-1_8.'
mla: Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization
Tasks.” Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag,
2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8.
short: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem
Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
111–124.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:53Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_8
extern: '1'
keyword:
- Continuous optimization
- Fully parallel search
- One-shot optimization
- Regression
- Surrogate-assisted optimization
language:
- iso: eng
page: 111–124
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: Evolving Sampling Strategies for One-Shot Optimization Tasks
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48851'
abstract:
- lang: eng
text: Several important optimization problems in the area of vehicle routing can
be seen as variants of the classical Traveling Salesperson Problem (TSP). In the
area of evolutionary computation, the Traveling Thief Problem (TTP) has gained
increasing interest over the last 5 years. In this paper, we investigate the effect
of weights on such problems, in the sense that the cost of traveling increases
with respect to the weights of nodes already visited during a tour. This provides
abstractions of important TSP variants such as the Traveling Thief Problem and
time dependent TSP variants, and allows to study precisely the increase in difficulty
caused by weight dependence. We provide a 3.59-approximation for this weight dependent
version of TSP with metric distances and bounded positive weights. Furthermore,
we conduct experimental investigations for simple randomized local search with
classical mutation operators and two variants of the state-of-the-art evolutionary
algorithm EAX adapted to the weighted TSP. Our results show the impact of the
node weights on the position of the nodes in the resulting tour.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Katrin
full_name: Casel, Katrin
last_name: Casel
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling
Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243'
apa: 'Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight
Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized
Search Heuristics. Proceedings of the Genetic and Evolutionary Computation
Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243'
bibtex: '@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY,
USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson
Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={10.1145/3377930.3390243},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel,
Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294},
collection={GECCO ’20} }'
chicago: 'Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The
Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 1286–1294. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390243.'
ieee: 'J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent
Traveling Salesperson Problem: Approximation Algorithms and Randomized Search
Heuristics,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 1286–1294, doi: 10.1145/3377930.3390243.'
mla: 'Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem:
Approximation Algorithms and Randomized Search Heuristics.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.'
short: 'J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1286–1294.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:33Z
department:
- _id: '819'
doi: 10.1145/3377930.3390243
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1286–1294
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48845'
abstract:
- lang: eng
text: 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 have to be planned short while the number of serviced customers
has to be maximized at the same time resulting in a multi-objective problem. Beyond
that, however, dynamic requests lead to the need for re-planning of not yet realized
tour parts, while already realized tour parts are irreversible. In this paper
we study this type of bi-objective dynamic VRP including sequential decision making
and concurrent realization of decisions. We adopt a recently proposed Dynamic
Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend
it to the more realistic (here considered) scenario of multiple vehicles. We empirically
show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant
variant of the proposed DEMOA as well as with the dynamic single-vehicle approach
proposed earlier.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple
Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146'
apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing
of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation
Conference, 166–174. https://doi.org/10.1145/3377930.3390146
bibtex: '@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA},
series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles},
DOI={10.1145/3377930.3390146},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective
Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 166–174. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390146.'
ieee: 'J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of
Multiple Vehicles,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2020, pp. 166–174, doi: 10.1145/3377930.3390146.'
mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2020, pp. 166–174, doi:10.1145/3377930.3390146.
short: 'J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 166–174.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:24Z
department:
- _id: '819'
doi: 10.1145/3377930.3390146
extern: '1'
keyword:
- decision making
- dynamic optimization
- evolutionary algorithms
- multi-objective optimization
- vehicle routing
language:
- iso: eng
page: 166–174
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: Dynamic Bi-Objective Routing of Multiple Vehicles
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48844'
abstract:
- lang: eng
text: 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 respective (restart) variants manage to calculate close-to optimal
or even optimal solutions, also for large instances with several thousand nodes
in reasonable time. In this work we extend existing benchmarking studies by addressing
anytime behaviour of inexact TSP solvers based on empirical runtime distributions
leading to an increased understanding of solver behaviour and the respective relation
to problem hardness. It turns out that performance ranking of solvers is highly
dependent on the focused approximation quality. Insights on intersection points
of performances offer huge potential for the construction of hybridized solvers
depending on instance features. Moreover, instance features tailored to anytime
performance and corresponding performance indicators will highly improve automated
algorithm selection models by including comprehensive information on solver quality.
author:
- 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: 'Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers
and Perspectives for Automated Algorithm Selection. In: 2020 IEEE Congress
on Evolutionary Computation (CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185613'
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact
TSP Solvers and Perspectives for Automated Algorithm Selection. 2020 IEEE Congress
on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185613
bibtex: '@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, United Kingdom},
title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated
Algorithm Selection}, DOI={10.1109/CEC48606.2020.9185613},
booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE
Press}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020},
pages={1–8} }'
chicago: 'Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior
of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In
2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, United
Kingdom: IEEE Press, 2020. https://doi.org/10.1109/CEC48606.2020.9185613.'
ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP
Solvers and Perspectives for Automated Algorithm Selection,” in 2020 IEEE Congress
on Evolutionary Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185613.'
mla: Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives
for Automated Algorithm Selection.” 2020 IEEE Congress on Evolutionary Computation
(CEC), IEEE Press, 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185613.
short: 'J. Bossek, P. Kerschke, H. Trautmann, in: 2020 IEEE Congress on Evolutionary
Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp. 1–8.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:16Z
department:
- _id: '819'
doi: 10.1109/CEC48606.2020.9185613
extern: '1'
language:
- iso: eng
page: 1–8
place: Glasgow, United Kingdom
publication: 2020 IEEE Congress on Evolutionary Computation (CEC)
publication_status: published
publisher: IEEE Press
status: public
title: Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm
Selection
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48850'
abstract:
- lang: eng
text: Sequential model-based optimization (SMBO) approaches are algorithms for solving
problems that require computationally or otherwise expensive function evaluations.
The key design principle of SMBO is a substitution of the true objective function
by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO
algorithms are intrinsically modular, leaving the user with many important design
choices. Significant research efforts go into understanding which settings perform
best for which type of problems. Most works, however, focus on the choice of the
model, the acquisition function, and the strategy used to optimize the latter.
The choice of the initial sampling strategy, however, receives much less attention.
Not surprisingly, quite diverging recommendations can be found in the literature.
We analyze in this work how the size and the distribution of the initial sample
influences the overall quality of the efficient global optimization (EGO) algorithm,
a well-known SMBO approach. While, overall, small initial budgets using Halton
sampling seem preferable, we also observe that the performance landscape is rather
unstructured. We furthermore identify several situations in which EGO performs
unfavorably against random sampling. Both observations indicate that an adaptive
SMBO design could be beneficial, making SMBO an interesting test-bed for automated
algorithm design.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects
on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:10.1145/3377930.3390155'
apa: 'Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. https://doi.org/10.1145/3377930.3390155'
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO
’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based
Optimization: An Exploratory Case Study Based on BBOB}, DOI={10.1145/3377930.3390155},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr,
Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB.” In Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. GECCO ’20. New York, NY, USA: Association for Computing
Machinery, 2020. https://doi.org/10.1145/3377930.3390155.'
ieee: 'J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their
Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based
on BBOB,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 778–786, doi: 10.1145/3377930.3390155.'
mla: 'Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential
Model-Based Optimization: An Exploratory Case Study Based on BBOB.” Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 778–786, doi:10.1145/3377930.3390155.'
short: 'J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 778–786.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:01Z
department:
- _id: '819'
doi: 10.1145/3377930.3390155
extern: '1'
keyword:
- continuous black-box optimization
- design of experiments
- initial design
- sequential model-based optimization
language:
- iso: eng
page: 778–786
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'Initial Design Strategies and Their Effects on Sequential Model-Based Optimization:
An Exploratory Case Study Based on BBOB'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48852'
abstract:
- lang: eng
text: The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial
optimisation problems. However, many real-world problems are composed of several
interacting components. The Traveling Thief Problem (TTP) addresses such interactions
by combining two combinatorial optimisation problems, namely the TSP and the Knapsack
Problem (KP). Recently, a new problem called the node weight dependent Traveling
Salesperson Problem (W-TSP) has been introduced where nodes have weights that
influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate
the structure of the optimised tours for W-TSP and TTP and the impact of using
each others fitness function. Our experimental results suggest (1) that the W-TSP
often can be solved better using the TTP fitness function and (2) final W-TSP
and TTP solutions show different distributions when compared with optimal TSP
or weighted greedy solutions.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann A, Neumann F. Optimising Tours for the Weighted Traveling
Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of
Solutions. In: Parallel Problem Solving from Nature (PPSN XVI). Springer-Verlag;
2020:346–359. doi:10.1007/978-3-030-58112-1_24'
apa: 'Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the
Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural
Comparison of Solutions. Parallel Problem Solving from Nature (PPSN XVI),
346–359. https://doi.org/10.1007/978-3-030-58112-1_24'
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2020, place={Berlin, Heidelberg},
title={Optimising Tours for the Weighted Traveling Salesperson Problem and the
Traveling Thief Problem: A Structural Comparison of Solutions}, DOI={10.1007/978-3-030-58112-1_24},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={346–359}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Optimising Tours for
the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A
Structural Comparison of Solutions.” In Parallel Problem Solving from Nature
(PPSN XVI), 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. https://doi.org/10.1007/978-3-030-58112-1_24.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted
Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison
of Solutions,” in Parallel Problem Solving from Nature (PPSN XVI), 2020,
pp. 346–359, doi: 10.1007/978-3-030-58112-1_24.'
mla: 'Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson
Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.”
Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, 2020,
pp. 346–359, doi:10.1007/978-3-030-58112-1_24.'
short: 'J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature
(PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:54Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_24
extern: '1'
keyword:
- Evolutionary algorithms
- Node weight dependent TSP
- Traveling Thief Problem
language:
- iso: eng
page: 346–359
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: 'Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling
Thief Problem: A Structural Comparison of Solutions'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48846'
abstract:
- lang: eng
text: 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 of unserved dynamic requests is minimized by a dynamic
evolutionary multi-objective algorithm (DEMOA), which operates on discrete time
windows (eras). A decision is made at each era by a decision-maker, thus any decision
depends on irreversible decisions made in foregoing eras. To understand effects
of sequences of decision-making and interactions/dependencies between decisions
made, we conduct a series of experiments. More precisely, we fix a set of decision-maker
preferences D and the number of eras n{$<$}inf{$>$}t{$<$}/inf{$>$} and analyze
all $|D|\^{n_t}$ combinations of decision-maker options. We find that for random
uniform instances (a) the final selected solutions mainly depend on the final
decision and not on the decision history, (b) solutions are quite robust with
respect to the number of unvisited dynamic customers, and (c) solutions of the
dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In
contrast, for instances with clustered customers, we observe a strong dependency
on decision-making history as well as more variance in solution diversity.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic
Bi-Objective Vehicle Routing. In: 2020 IEEE Congress on Evolutionary Computation
(CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185778'
apa: Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision
Support in Dynamic Bi-Objective Vehicle Routing. 2020 IEEE Congress on Evolutionary
Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
bibtex: '@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, United
Kingdom}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing},
DOI={10.1109/CEC48606.2020.9185778},
booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE
Press}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann,
Heike}, year={2020}, pages={1–8} }'
chicago: 'Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann.
“Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In 2020
IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, United Kingdom:
IEEE Press, 2020. https://doi.org/10.1109/CEC48606.2020.9185778.'
ieee: 'J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support
in Dynamic Bi-Objective Vehicle Routing,” in 2020 IEEE Congress on Evolutionary
Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185778.'
mla: Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle
Routing.” 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press,
2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778.
short: 'J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: 2020 IEEE Congress on
Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp.
1–8.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:17Z
department:
- _id: '819'
doi: 10.1109/CEC48606.2020.9185778
extern: '1'
language:
- iso: eng
page: 1–8
place: Glasgow, United Kingdom
publication: 2020 IEEE Congress on Evolutionary Computation (CEC)
publication_status: published
publisher: IEEE Press
status: public
title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48879'
abstract:
- lang: eng
text: Evolving diverse sets of high quality solutions has gained increasing interest
in the evolutionary computation literature in recent years. With this paper, we
contribute to this area of research by examining evolutionary diversity optimisation
approaches for the classical Traveling Salesperson Problem (TSP). We study the
impact of using different diversity measures for a given set of tours and the
ability of evolutionary algorithms to obtain a diverse set of high quality solutions
when adopting these measures. Our studies show that a large variety of diverse
high quality tours can be achieved by using our approaches. Furthermore, we compare
our approaches in terms of theoretical properties and the final set of tours obtained
by the evolutionary diversity optimisation algorithm.
author:
- first_name: Anh Viet
full_name: Do, Anh Viet
last_name: Do
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for
the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689.
doi:10.1145/3377930.3389844'
apa: Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse
Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic
and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844
bibtex: '@inproceedings{Do_Bossek_Neumann_Neumann_2020, place={New York, NY, USA},
series={GECCO’20}, title={Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem}, DOI={10.1145/3377930.3389844},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Do, Anh Viet and Bossek,
Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={681–689}, collection={GECCO’20}
}'
chicago: 'Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving
Diverse Sets of Tours for the Travelling Salesperson Problem.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 681–689. GECCO’20.
New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3389844.'
ieee: 'A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of
Tours for the Travelling Salesperson Problem,” in Proceedings of the Genetic
and Evolutionary Computation Conference, 2020, pp. 681–689, doi: 10.1145/3377930.3389844.'
mla: Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem.” Proceedings of the Genetic and Evolutionary Computation Conference,
Association for Computing Machinery, 2020, pp. 681–689, doi:10.1145/3377930.3389844.
short: 'A.V. Do, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 681–689.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:48:50Z
department:
- _id: '819'
doi: 10.1145/3377930.3389844
extern: '1'
keyword:
- diversity maximisation
- evolutionary algorithms
- travelling salesperson problem
language:
- iso: eng
page: 681–689
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publisher: Association for Computing Machinery
series_title: GECCO’20
status: public
title: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48895'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) are general-purpose problem solvers that usually
perform an unbiased search. This is reasonable and desirable in a black-box scenario.
For combinatorial optimization problems, often more knowledge about the structure
of optimal solutions is given, which can be leveraged by means of biased search
operators. We consider the Minimum Spanning Tree (MST) problem in a single- and
multi-objective version, and introduce a biased mutation, which puts more emphasis
on the selection of edges of low rank in terms of low domination number. We present
example graphs where the biased mutation can significantly speed up the expected
runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate
that bias can lead to exponential runtime if "heavy" edges are necessarily part
of an optimal solution. However, on general graphs in the single-objective setting,
we show that a combined mutation operator which decides for unbiased or biased
edge selection in each step with equal probability exhibits a polynomial upper
bound - as unbiased mutation - in the worst case and benefits from bias if the
circumstances are favorable.
author:
- first_name: Vahid
full_name: Roostapour, Vahid
last_name: Roostapour
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms
with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In:
Proceedings of the 2020 Genetic and Evolutionary Computation Conference.
{GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168'
apa: Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
551–559. https://doi.org/10.1145/3377930.3390168
bibtex: '@inproceedings{Roostapour_Bossek_Neumann_2020, place={New York, NY, USA},
series={{GECCO} ’20}, title={Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}, DOI={10.1145/3377930.3390168}, booktitle={Proceedings
of the 2020 Genetic and Evolutionary Computation Conference}, publisher={Association
for Computing Machinery}, author={Roostapour, Vahid and Bossek, Jakob and Neumann,
Frank}, year={2020}, pages={551–559}, collection={{GECCO} ’20} }'
chicago: 'Roostapour, Vahid, Jakob Bossek, and Frank Neumann. “Runtime Analysis
of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum
Spanning Tree Problem.” In Proceedings of the 2020 Genetic and Evolutionary
Computation Conference, 551–559. {GECCO} ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390168.'
ieee: 'V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
2020, pp. 551–559, doi: 10.1145/3377930.3390168.'
mla: Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” Proceedings
of the 2020 Genetic and Evolutionary Computation Conference, Association for
Computing Machinery, 2020, pp. 551–559, doi:10.1145/3377930.3390168.
short: 'V. Roostapour, J. Bossek, F. Neumann, in: Proceedings of the 2020 Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 551–559.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:38Z
department:
- _id: '819'
doi: 10.1145/3377930.3390168
extern: '1'
keyword:
- biased mutation
- evolutionary algorithms
- minimum spanning tree problem
- runtime analysis
language:
- iso: eng
page: 551–559
place: New York, NY, USA
publication: Proceedings of the 2020 Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publisher: Association for Computing Machinery
series_title: '{GECCO} ’20'
status: public
title: Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective
Minimum Spanning Tree Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_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: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag;
2020:48–64. doi:10.1007/978-3-030-58112-1_4'
apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection
on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature}
(PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
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={10.1007/978-3-030-58112-1_4},
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 Parallel Problem
Solving from {Nature} (PPSN XVI), 48–64. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_4.'
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 Parallel Problem Solving from {Nature} (PPSN
XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.'
mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach
for Automated Algorithm Selection on the Traveling Salesperson Problem.” Parallel
Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, 2020, pp. 48–64,
doi:10.1007/978-3-030-58112-1_4.
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'
...
---
_id: '48848'
abstract:
- lang: eng
text: We build upon a recently proposed multi-objective view onto performance measurement
of single-objective stochastic solvers. The trade-off between the fraction of
failed runs and the mean runtime of successful runs \textendash both to be minimized
\textendash is directly analyzed based on a study on algorithm selection of inexact
state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover,
we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization
for simultaneously assessing both conflicting objectives and investigate relations
to commonly used performance indicators, both theoretically and empirically. Next
to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV
measure is used as a core concept within the construction of per-instance algorithm
selection models offering interesting insights into complementary behavior of
inexact TSP solvers. \textbullet The multi-objective perspective is naturally
generalizable to multiple objectives. \textbullet Proof of relationship between
HV and the PAR in the considered bi-objective space. \textbullet New insights
into complementary behavior of stochastic optimization algorithms.
author:
- 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: Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance
Assessment and Automated Selection of Single-Objective Optimization Algorithms.
Applied Soft Computing. 2020;88(C). doi:10.1016/j.asoc.2019.105901
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
bibtex: '@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms}, volume={88}, DOI={10.1016/j.asoc.2019.105901},
number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke,
Pascal and Trautmann, Heike}, year={2020} }'
chicago: Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective
Perspective on Performance Assessment and Automated Selection of Single-Objective
Optimization Algorithms.” Applied Soft Computing 88, no. C (2020). https://doi.org/10.1016/j.asoc.2019.105901.
ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective
on Performance Assessment and Automated Selection of Single-Objective Optimization
Algorithms,” Applied Soft Computing, vol. 88, no. C, 2020, doi: 10.1016/j.asoc.2019.105901.'
mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment
and Automated Selection of Single-Objective Optimization Algorithms.” Applied
Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901.
short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:52:17Z
department:
- _id: '819'
doi: 10.1016/j.asoc.2019.105901
intvolume: ' 88'
issue: C
keyword:
- Algorithm selection
- Combinatorial optimization
- Multi-objective optimization
- Performance measurement
- Traveling Salesperson Problem
language:
- iso: eng
publication: Applied Soft Computing
publication_identifier:
issn:
- 1568-4946
status: public
title: A Multi-Objective Perspective on Performance Assessment and Automated Selection
of Single-Objective Optimization Algorithms
type: journal_article
user_id: '102979'
volume: 88
year: '2020'
...
---
_id: '48836'
author:
- first_name: Thomas
full_name: Bartz-Beielstein, Thomas
last_name: Bartz-Beielstein
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Daan
full_name: van den Berg, Daan
last_name: van den Berg
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Sowmya
full_name: Chandrasekaran, Sowmya
last_name: Chandrasekaran
- first_name: Tome
full_name: Eftimov, Tome
last_name: Eftimov
- first_name: Andreas
full_name: Fischbach, Andreas
last_name: Fischbach
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: William La
full_name: Cava, William La
last_name: Cava
- first_name: Manuel
full_name: Lopez-Ibanez, Manuel
last_name: Lopez-Ibanez
- first_name: Katherine M.
full_name: Malan, Katherine M.
last_name: Malan
- first_name: Jason H.
full_name: Moore, Jason H.
last_name: Moore
- first_name: Boris
full_name: Naujoks, Boris
last_name: Naujoks
- first_name: Patryk
full_name: Orzechowski, Patryk
last_name: Orzechowski
- first_name: Vanessa
full_name: Volz, Vanessa
last_name: Volz
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Thomas
full_name: Weise, Thomas
last_name: Weise
citation:
ama: 'Bartz-Beielstein T, Doerr C, van den Berg D, et al. Benchmarking in Optimization:
Best Practice and Open Issues. Corr. Published online 2020.'
apa: 'Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran,
S., Eftimov, T., Fischbach, A., Kerschke, P., Cava, W. L., Lopez-Ibanez, M., Malan,
K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., &
Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues.
Corr.'
bibtex: '@article{Bartz-Beielstein_Doerr_van den Berg_Bossek_Chandrasekaran_Eftimov_Fischbach_Kerschke_Cava_Lopez-Ibanez_et
al._2020, title={Benchmarking in Optimization: Best Practice and Open Issues},
journal={Corr}, author={Bartz-Beielstein, Thomas and Doerr, Carola and van den
Berg, Daan and Bossek, Jakob and Chandrasekaran, Sowmya and Eftimov, Tome and
Fischbach, Andreas and Kerschke, Pascal and Cava, William La and Lopez-Ibanez,
Manuel and et al.}, year={2020} }'
chicago: 'Bartz-Beielstein, Thomas, Carola Doerr, Daan van den Berg, Jakob Bossek,
Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, et al. “Benchmarking in
Optimization: Best Practice and Open Issues.” Corr, 2020.'
ieee: 'T. Bartz-Beielstein et al., “Benchmarking in Optimization: Best Practice
and Open Issues,” Corr, 2020.'
mla: 'Bartz-Beielstein, Thomas, et al. “Benchmarking in Optimization: Best Practice
and Open Issues.” Corr, 2020.'
short: T. Bartz-Beielstein, C. Doerr, D. van den Berg, J. Bossek, S. Chandrasekaran,
T. Eftimov, A. Fischbach, P. Kerschke, W.L. Cava, M. Lopez-Ibanez, K.M. Malan,
J.H. Moore, B. Naujoks, P. Orzechowski, V. Volz, M. Wagner, T. Weise, Corr (2020).
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:52:24Z
department:
- _id: '819'
language:
- iso: eng
publication: Corr
status: public
title: 'Benchmarking in Optimization: Best Practice and Open Issues'
type: journal_article
user_id: '102979'
year: '2020'
...