---
_id: '46325'
abstract:
- lang: eng
text: Clustering is an important technique in data analysis which can reveal hidden
patterns and unknown relationships in the data. A common problem in clustering
is the proper choice of parameter settings. To tackle this, automated algorithm
configuration is available which can automatically find the best parameter settings.
In practice, however, many of our today’s data sources are data streams due to
the widespread deployment of sensors, the internet-of-things or (social) media.
Stream clustering aims to tackle this challenge by identifying, tracking and updating
clusters over time. Unfortunately, none of the existing approaches for automated
algorithm configuration are directly applicable to the streaming scenario. In
this paper, we explore the possibility of automated algorithm configuration for
stream clustering algorithms using an ensemble of different configurations. In
first experiments, we demonstrate that our approach is able to automatically find
superior configurations and refine them over time.
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. Towards Automated Configuration
of Stream Clustering Algorithms. In: Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECMLPKDD ’19). ; 2020:137–143. doi:10.1007/978-3-030-43823-4_12'
apa: Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). Towards
Automated Configuration of Stream Clustering Algorithms. Proceedings of the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECMLPKDD ’19), 137–143. https://doi.org/10.1007/978-3-030-43823-4_12
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg,
Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms},
DOI={10.1007/978-3-030-43823-4_12},
booktitle={Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein,
Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020},
pages={137–143} }'
chicago: Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“Towards Automated Configuration of Stream Clustering Algorithms.” In Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Databases (ECMLPKDD ’19), 137–143. Würzburg, Germany,
2020. https://doi.org/10.1007/978-3-030-43823-4_12.
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated
Configuration of Stream Clustering Algorithms,” in Proceedings of the European
Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Databases (ECMLPKDD ’19), 2020, pp. 137–143, doi: 10.1007/978-3-030-43823-4_12.'
mla: Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering
Algorithms.” Proceedings of the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19),
2020, pp. 137–143, doi:10.1007/978-3-030-43823-4_12.
short: 'M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.'
date_created: 2023-08-04T07:35:24Z
date_updated: 2023-10-16T13:03:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-43823-4_12
language:
- iso: eng
page: 137–143
place: Würzburg, Germany
publication: Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)
publication_identifier:
isbn:
- 978-3-030-43823-4
status: public
title: Towards Automated Configuration of Stream Clustering Algorithms
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46321'
abstract:
- lang: eng
text: Social bots have recently gained attention in the context of public opinion
manipulation on social media platforms. While a lot of research effort has been
put into the classification and detection of such automated programs, it is still
unclear how technically sophisticated those bots are, which platforms they target,
and where they originate from. To answer these questions, we gathered repository
data from open source collaboration platforms to identify the status-quo of social
bot development as well as first insights into the overall skills of publicly
available bot code.
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- 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
- first_name: Lena
full_name: Adam, Lena
last_name: Adam
citation:
ama: 'Assenmacher D, Frischlich L, Trautmann H, Grimme C, Adam L. Inside the tool
set of automation: Free social bot code revisited. In: Grimme C, Preuß M, Takes
F, Waldherr A, eds. Disinformation in Open Online Media. Lecture Notes
in Computer Science. Springer; 2020:101–114.'
apa: 'Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., & Adam, L.
(2020). Inside the tool set of automation: Free social bot code revisited. In
C. Grimme, M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in
open online media (pp. 101–114). Springer.'
bibtex: '@inproceedings{Assenmacher_Frischlich _Trautmann_Grimme_Adam_2020, place={Wiesbaden},
series={Lecture Notes in Computer Science}, title={Inside the tool set of automation:
Free social bot code revisited}, booktitle={Disinformation in open online media},
publisher={Springer}, author={Assenmacher, Dennis and Frischlich , Lena and Trautmann,
Heike and Grimme, Christian and Adam, Lena}, editor={Grimme, Christian and Preuß,
Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={101–114}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme,
and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.”
In Disinformation in Open Online Media, edited by Christian Grimme, Mike
Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science.
Wiesbaden: Springer, 2020.'
ieee: 'D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside
the tool set of automation: Free social bot code revisited,” in Disinformation
in open online media, 2020, pp. 101–114.'
mla: 'Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social
Bot Code Revisited.” Disinformation in Open Online Media, edited by Christian
Grimme et al., Springer, 2020, pp. 101–114.'
short: 'D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, L. Adam, in: C.
Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online
Media, Springer, Wiesbaden, 2020, pp. 101–114.'
date_created: 2023-08-04T07:31:13Z
date_updated: 2023-10-16T13:00:15Z
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: 101–114
place: Wiesbaden
publication: Disinformation in open online media
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Inside the tool set of automation: Free social bot code revisited'
type: conference
user_id: '15504'
year: '2020'
...
---
_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: '46331'
abstract:
- lang: eng
text: Artificial neural networks in general and deep learning networks in particular
established themselves as popular and powerful machine learning algorithms. While
the often tremendous sizes of these networks are beneficial when solving complex
tasks, the tremendous number of parameters also causes such networks to be vulnerable
to malicious behavior such as adversarial perturbations. These perturbations can
change a model's classification decision. Moreover, while single-step adversaries
can easily be transferred from network to network, the transfer of more powerful
multi-step adversaries has - usually - been rather difficult.In this work, we
introduce a method for generating strong adversaries that can easily (and frequently)
be transferred between different models. This method is then used to generate
a large set of adversaries, based on which the effects of selected defense methods
are experimentally assessed. At last, we introduce a novel, simple, yet effective
approach to enhance the resilience of neural networks against adversaries and
benchmark it against established defense methods. In contrast to the already existing
methods, our proposed defense approach is much more efficient as it only requires
a single additional forward-pass to achieve comparable performance results.
author:
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Seiler M, Trautmann H, Kerschke P. Enhancing Resilience of Deep Learning Networks
By Means of Transferable Adversaries. In: Proceedings of the International
Joint Conference on Neural Networks (IJCNN). ; 2020:1–8. doi:10.1109/IJCNN48605.2020.9207338'
apa: Seiler, M., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience
of Deep Learning Networks By Means of Transferable Adversaries. Proceedings
of the International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207338
bibtex: '@inproceedings{Seiler_Trautmann_Kerschke_2020, place={Glasgow, UK}, title={Enhancing
Resilience of Deep Learning Networks By Means of Transferable Adversaries}, DOI={10.1109/IJCNN48605.2020.9207338},
booktitle={Proceedings of the International Joint Conference on Neural Networks
(IJCNN)}, author={Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2020},
pages={1–8} }'
chicago: Seiler, Moritz, Heike Trautmann, and Pascal Kerschke. “Enhancing Resilience
of Deep Learning Networks By Means of Transferable Adversaries.” In Proceedings
of the International Joint Conference on Neural Networks (IJCNN), 1–8. Glasgow,
UK, 2020. https://doi.org/10.1109/IJCNN48605.2020.9207338.
ieee: 'M. Seiler, H. Trautmann, and P. Kerschke, “Enhancing Resilience of Deep Learning
Networks By Means of Transferable Adversaries,” in Proceedings of the International
Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8, doi: 10.1109/IJCNN48605.2020.9207338.'
mla: Seiler, Moritz, et al. “Enhancing Resilience of Deep Learning Networks By Means
of Transferable Adversaries.” Proceedings of the International Joint Conference
on Neural Networks (IJCNN), 2020, pp. 1–8, doi:10.1109/IJCNN48605.2020.9207338.
short: 'M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the International
Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.'
date_created: 2023-08-04T07:39:48Z
date_updated: 2024-06-07T07:11:53Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/IJCNN48605.2020.9207338
language:
- iso: eng
page: 1–8
place: Glasgow, UK
publication: Proceedings of the International Joint Conference on Neural Networks
(IJCNN)
status: public
title: Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46330'
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
1000 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
id: '105520'
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
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
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: Bäck T, Preuss M, Deutz A, et al., eds. Proceedings of the 16$^th$
International Conference on Parallel Problem Solving from Nature (PPSN XVI).
; 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. In T. Bäck, M. Preuss, A. Deutz, H. Wang,
C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$
International Conference on Parallel Problem Solving from Nature (PPSN XVI)
(pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
bibtex: '@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Leiden,
The Netherlands}, 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={Proceedings of the 16$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XVI)}, author={Seiler, Moritz and Pohl, Janina and Bossek,
Jakob and Kerschke, Pascal and Trautmann, Heike}, editor={Bäck, Thomas and Preuss,
Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael 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 Proceedings of the 16$^th$
International Conference on Parallel Problem Solving from Nature (PPSN XVI),
edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael
Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 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 Proceedings of the 16$^th$ International
Conference on 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.” Proceedings
of the 16$^th$ International Conference on Parallel Problem Solving from Nature
(PPSN XVI), edited by Thomas Bäck et al., 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: T. Bäck, M.
Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Proceedings
of the 16$^th$ International Conference on Parallel Problem Solving from Nature
(PPSN XVI), Leiden, The Netherlands, 2020, pp. 48–64.'
date_created: 2023-08-04T07:39:05Z
date_updated: 2024-06-10T11:57:13Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-58112-1_4
editor:
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André
full_name: Deutz, André
last_name: Deutz
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
language:
- iso: eng
page: 48–64
place: Leiden, The Netherlands
publication: Proceedings of the 16$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XVI)
status: public
title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm
Selection on the Traveling Salesperson Problem
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46334'
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 – both to be minimized – 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.
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
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
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;88:105901. doi:https://doi.org/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, 105901. 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={https://doi.org/10.1016/j.asoc.2019.105901},
journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and
Trautmann, Heike}, year={2020}, pages={105901} }'
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 (2020): 105901. 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, p. 105901, 2020, doi: https://doi.org/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, 2020, p. 105901, doi:https://doi.org/10.1016/j.asoc.2019.105901.
short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.
date_created: 2023-08-04T07:42:26Z
date_updated: 2024-06-10T12:00:46Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.asoc.2019.105901
intvolume: ' 88'
keyword:
- Algorithm selection
- Multi-objective optimization
- Performance measurement
- Combinatorial optimization
- Traveling Salesperson Problem
language:
- iso: eng
page: '105901'
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: '15504'
volume: 88
year: '2020'
...
---
_id: '46322'
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 t and analyze all |D| nt 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
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic
Bi-Objective Vehicle Routing. In: Proceedings of the IEEE Congress on Evolutionary
Computation (CEC). ; 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. Proceedings of the 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, UK},
title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={10.1109/CEC48606.2020.9185778},
booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
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 Proceedings of
the IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, UK, 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 Proceedings of the 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.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC),
2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778.
short: 'J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: Proceedings of the IEEE
Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, pp. 1–8.'
date_created: 2023-08-04T07:32:36Z
date_updated: 2024-06-10T12:02:05Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC48606.2020.9185778
language:
- iso: eng
page: 1–8
place: Glasgow, UK
publication: Proceedings of the IEEE Congress on Evolutionary Computation (CEC)
status: public
title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46324'
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
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers
and Perspectives for Automated Algorithm Selection. In: Proceedings of the
IEEE Congress on Evolutionary Computation (CEC). IEEE; 2020:1–8.'
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact
TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings
of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
bibtex: '@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, UK}, title={Anytime
Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection},
booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
publisher={IEEE}, 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
Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
Glasgow, UK: IEEE, 2020.'
ieee: J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP
Solvers and Perspectives for Automated Algorithm Selection,” in Proceedings
of the IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8.
mla: Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives
for Automated Algorithm Selection.” Proceedings of the IEEE Congress on Evolutionary
Computation (CEC), IEEE, 2020, pp. 1–8.
short: 'J. Bossek, P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress
on Evolutionary Computation (CEC), IEEE, Glasgow, UK, 2020, pp. 1–8.'
date_created: 2023-08-04T07:34:40Z
date_updated: 2024-06-10T12:01:46Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 1–8
place: Glasgow, UK
publication: Proceedings of the IEEE Congress on Evolutionary Computation (CEC)
publisher: IEEE
status: public
title: Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm
Selection
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46323'
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
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
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). ACM; 2020:166–174.'
apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing
of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO ’20), 166–174.
bibtex: '@inproceedings{Bossek_Grimme_Trautmann_2020, place={Cancun, Mexico}, title={Dynamic
Bi-Objective Routing of Multiple Vehicles}, booktitle={Proceedings of the Genetic
and Evolutionary Computation Conference (GECCO ’20)}, publisher={ACM}, author={Bossek,
Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174}
}'
chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective
Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO ’20), 166–174. Cancun, Mexico: ACM, 2020.'
ieee: J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple
Vehicles,” in Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’20), 2020, pp. 166–174.
mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20),
ACM, 2020, pp. 166–174.
short: 'J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO ’20), ACM, Cancun, Mexico, 2020, pp. 166–174.'
date_created: 2023-08-04T07:33:30Z
date_updated: 2024-06-10T12:01:57Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 166–174
place: Cancun, Mexico
publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’20)
publisher: ACM
status: public
title: Dynamic Bi-Objective Routing of Multiple Vehicles
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46343'
abstract:
- lang: eng
text: This paper addresses multimodality of multi-objective (MO) optimization landscapes.
Contrary to common perception of local optima, according to which they are hindering
the progress of optimization algorithms, it will be shown that local efficient
sets in a multi-objective setting can assist optimizers in finding global efficient
sets. We use sophisticated visualization techniques, which rely on gradient field
heatmaps, to highlight those insights into landscape characteristics. Finally,
the MO local optimizer MOGSA is introduced, which exploits those observations
by sliding down the multi-objective gradient hill and moving along the local efficient
sets.
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization
— More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. Proceedings
of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization
(EMO). Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138.
doi:10.1007/978-3-030-12598-1_11'
apa: Grimme, C., Kerschke, P., & Trautmann, H. (2019). Multimodality in Multi-Objective
Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K.
Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Proceedings of
the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization
(EMO) (Vol. 11411, pp. 126–138). Springer. https://doi.org/10.1007/978-3-030-12598-1_11
bibtex: '@inproceedings{Grimme_Kerschke_Trautmann_2019, place={East Lansing, MI,
USA}, series={Lecture Notes in Computer Science}, title={Multimodality in Multi-Objective
Optimization — More Boon than Bane?}, volume={11411}, DOI={10.1007/978-3-030-12598-1_11},
booktitle={Proceedings of the 10$^th$ International Conference on Evolutionary
Multi-Criterion Optimization (EMO)}, publisher={Springer}, author={Grimme, Christian
and Kerschke, Pascal and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman,
Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and
Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={126–138}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Grimme, Christian, Pascal Kerschke, and Heike Trautmann. “Multimodality
in Multi-Objective Optimization — More Boon than Bane?” In Proceedings of the
10$^th$ International Conference on Evolutionary Multi-Criterion Optimization
(EMO), edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin
Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:126–138. Lecture
Notes in Computer Science. East Lansing, MI, USA: Springer, 2019. https://doi.org/10.1007/978-3-030-12598-1_11.'
ieee: 'C. Grimme, P. Kerschke, and H. Trautmann, “Multimodality in Multi-Objective
Optimization — More Boon than Bane?,” in Proceedings of the 10$^th$ International
Conference on Evolutionary Multi-Criterion Optimization (EMO), 2019, vol.
11411, pp. 126–138, doi: 10.1007/978-3-030-12598-1_11.'
mla: Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization —
More Boon than Bane?” Proceedings of the 10$^th$ International Conference on
Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et
al., vol. 11411, Springer, 2019, pp. 126–138, doi:10.1007/978-3-030-12598-1_11.
short: 'C. Grimme, P. Kerschke, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello,
K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Proceedings of the 10$^th$
International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer,
East Lansing, MI, USA, 2019, pp. 126–138.'
date_created: 2023-08-04T07:49:08Z
date_updated: 2023-10-16T13:31:03Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-12598-1_11
editor:
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Erik
full_name: Goodman, Erik
last_name: Goodman
- first_name: Coello Carlos A.
full_name: Coello, Coello Carlos A.
last_name: Coello
- first_name: Kathrin
full_name: Klamroth, Kathrin
last_name: Klamroth
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Patrick
full_name: Reed, Patrick
last_name: Reed
intvolume: ' 11411'
language:
- iso: eng
page: 126–138
place: East Lansing, MI, USA
publication: Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion
Optimization (EMO)
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Multimodality in Multi-Objective Optimization — More Boon than Bane?
type: conference
user_id: '15504'
volume: 11411
year: '2019'
...
---
_id: '46345'
abstract:
- lang: eng
text: It has long been observed that for practically any computational problem that
has been intensely studied, different instances are best solved using different
algorithms. This is particularly pronounced for computationally hard problems,
where in most cases, no single algorithm defines the state of the art; instead,
there is a set of algorithms with complementary strengths. This performance complementarity
can be exploited in various ways, one of which is based on the idea of selecting,
from a set of given algorithms, for each problem instance to be solved the one
expected to perform best. The task of automatically selecting an algorithm from
a given set is known as the per-instance algorithm selection problem and has been
intensely studied over the past 15 years, leading to major improvements in the
state of the art in solving a growing number of discrete combinatorial problems,
including propositional satisfiability and AI planning. Per-instance algorithm
selection also shows much promise for boosting performance in solving continuous
and mixed discrete/continuous optimisation problems. This survey provides an overview
of research in automated algorithm selection, ranging from early and seminal works
to recent and promising application areas. Different from earlier work, it covers
applications to discrete and continuous problems, and discusses algorithm selection
in context with conceptually related approaches, such as algorithm configuration,
scheduling, or portfolio selection. Since informative and cheaply computable problem
instance features provide the basis for effective per-instance algorithm selection
systems, we also provide an overview of such features for discrete and continuous
problems. Finally, we provide perspectives on future work in the area and discuss
a number of open research challenges.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Holger H
full_name: Hoos, Holger H
last_name: Hoos
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection:
Survey and Perspectives. Evolutionary Computation (ECJ). 2019;27(1):3–45.
doi:10.1162/evco_a_00242'
apa: 'Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated
Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ),
27(1), 3–45. https://doi.org/10.1162/evco_a_00242'
bibtex: '@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm
Selection: Survey and Perspectives}, volume={27}, DOI={10.1162/evco_a_00242},
number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45}
}'
chicago: 'Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated
Algorithm Selection: Survey and Perspectives.” Evolutionary Computation (ECJ)
27, no. 1 (2019): 3–45. https://doi.org/10.1162/evco_a_00242.'
ieee: 'P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm
Selection: Survey and Perspectives,” Evolutionary Computation (ECJ), vol.
27, no. 1, pp. 3–45, 2019, doi: 10.1162/evco_a_00242.'
mla: 'Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.”
Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 3–45, doi:10.1162/evco_a_00242.'
short: P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation
(ECJ) 27 (2019) 3–45.
date_created: 2023-08-04T07:50:33Z
date_updated: 2023-10-16T13:31:40Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00242
intvolume: ' 27'
issue: '1'
language:
- iso: eng
page: 3–45
publication: Evolutionary Computation (ECJ)
status: public
title: 'Automated Algorithm Selection: Survey and Perspectives'
type: journal_article
user_id: '15504'
volume: 27
year: '2019'
...
---
_id: '46344'
abstract:
- lang: eng
text: Analyzing data streams has received considerable attention over the past decades
due to the widespread usage of sensors, social media and other streaming data
sources. A core research area in this field is stream clustering which aims to
recognize patterns in an unordered, infinite and evolving stream of observations.
Clustering can be a crucial support in decision making, since it aims for an optimized
aggregated representation of a continuous data stream over time and allows to
identify patterns in large and high-dimensional data. A multitude of algorithms
and approaches has been developed that are able to find and maintain clusters
over time in the challenging streaming scenario. This survey explores, summarizes
and categorizes a total of 51 stream clustering algorithms and identifies core
research threads over the past decades. In particular, it identifies categories
of algorithms based on distance thresholds, density grids and statistical models
as well as algorithms for high dimensional data. Furthermore, it discusses applications
scenarios, available software and how to configure stream clustering algorithms.
This survey is considerably more extensive than comparable studies, more up-to-date
and highlights how concepts are interrelated and have been developed over time.
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
citation:
ama: 'Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive
Survey on Stream Clustering Algorithms. Business and Information Systems Engineering
(BISE). 2019;61(3):277–297.'
apa: 'Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms. Business and Information
Systems Engineering (BISE), 61(3), 277–297.'
bibtex: '@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3},
journal={Business and Information Systems Engineering (BISE)}, author={Carnein,
Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }'
chicago: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms.” Business and Information
Systems Engineering (BISE) 61, no. 3 (2019): 277–297.'
ieee: 'M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive
Survey on Stream Clustering Algorithms,” Business and Information Systems Engineering
(BISE), vol. 61, no. 3, pp. 277–297, 2019.'
mla: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation:
An Extensive Survey on Stream Clustering Algorithms.” Business and Information
Systems Engineering (BISE), vol. 61, no. 3, 2019, pp. 277–297.'
short: M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE)
61 (2019) 277–297.
date_created: 2023-08-04T07:49:47Z
date_updated: 2023-10-16T13:31:21Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 61'
issue: '3'
language:
- iso: eng
page: 277–297
publication: Business and Information Systems Engineering (BISE)
status: public
title: 'Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering
Algorithms'
type: journal_article
user_id: '15504'
volume: 61
year: '2019'
...
---
_id: '46340'
abstract:
- lang: eng
text: Recommender systems aim to provide personalized suggestions to customers which
products to buy or services to consume. They can help to increase sales by helping
customers discover new and relevant products. Traditionally, recommender systems
use the purchase history of a customer, e.g., the purchased quantity or properties
of the items. While this allows to build personalized recommendations, it is a
very limited view of the problem. Nowadays, extensive information about customers
and their personal preferences is available which goes far beyond their purchase
behaviour. For example, customers reveal their preferences in social media, by
their browsing habits and online search behaviour or their interest in specific
newsletters. In this paper, we investigate how information from different sources
and channels can be collected and incorporated into the recommendation process.
We demonstrate this, based on a real-life case study of a retailer with several
million transactions. We discuss how to employ a recommender system in this scenario,
evaluate various recommendation strategies and describe how to incorporate information
from different sources and channels, both internal and external. Our results show
that the recommendations can be better tailored to the personal preferences of
customers.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Leschek
full_name: Homann, Leschek
last_name: Homann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Gottfried
full_name: Vossen, Gottfried
last_name: Vossen
citation:
ama: 'Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on
Omni-Channel Customer Data. In: Proceedings of the 21$^st$ IEEE Conference
on Business Informatics (CBI’ 19). ; 2019:65–74.'
apa: Carnein, M., Homann, L., Trautmann, H., & Vossen, G. (2019). A Recommender
System Based on Omni-Channel Customer Data. Proceedings of the 21$^st$ IEEE
Conference on Business Informatics (CBI’ 19), 65–74.
bibtex: '@inproceedings{Carnein_Homann_Trautmann_Vossen_2019, place={Moscow, Russia},
title={A Recommender System Based on Omni-Channel Customer Data}, booktitle={Proceedings
of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}, author={Carnein,
Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}, year={2019},
pages={65–74} }'
chicago: Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen.
“A Recommender System Based on Omni-Channel Customer Data.” In Proceedings
of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 65–74. Moscow,
Russia, 2019.
ieee: M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System
Based on Omni-Channel Customer Data,” in Proceedings of the 21$^st$ IEEE Conference
on Business Informatics (CBI’ 19), 2019, pp. 65–74.
mla: Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer
Data.” Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’
19), 2019, pp. 65–74.
short: 'M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$
IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.'
date_created: 2023-08-04T07:46:20Z
date_updated: 2023-10-16T13:29:53Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 65–74
place: Moscow, Russia
publication: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’
19)
status: public
title: A Recommender System Based on Omni-Channel Customer Data
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46341'
abstract:
- lang: eng
text: Customer Segmentation aims to identify groups of customers that share similar
interest or behaviour. It is an essential tool in marketing and can be used to
target customer segments with tailored marketing strategies. Customer segmentation
is often based on clustering techniques. This analysis is typically performed
as a snapshot analysis where segments are identified at a specific point in time.
However, this ignores the fact that customer segments are highly volatile and
segments change over time. Once segments change, the entire analysis needs to
be repeated and strategies adapted. In this paper we explore stream clustering
as a tool to alleviate this problem. We propose a new stream clustering algorithm
which allows to identify and track customer segments over time. The biggest challenge
is that customer segmentation often relies on the transaction history of a customer.
Since this data changes over time, it is necessary to update customers which have
already been incorporated into the clustering. We show how to perform this step
incrementally, without the need for periodic re-computations. As a result, customer
segmentation can be performed continuously, faster and is more scalable. We demonstrate
the performance of our algorithm using a large real-life case study.
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
citation:
ama: 'Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data
Using Stream Clustering. In: Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19). ; 2019:280–292.'
apa: Carnein, M., & Trautmann, H. (2019). Customer Segmentation Based on Transactional
Data Using Stream Clustering. Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 280–292.
bibtex: '@inproceedings{Carnein_Trautmann_2019, place={Macau, China}, title={Customer
Segmentation Based on Transactional Data Using Stream Clustering}, booktitle={Proceedings
of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={280–292}
}'
chicago: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on
Transactional Data Using Stream Clustering.” In Proceedings of the 23$^rd$
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19),
280–292. Macau, China, 2019.
ieee: M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional
Data Using Stream Clustering,” in Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292.
mla: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional
Data Using Stream Clustering.” Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292.
short: 'M. Carnein, H. Trautmann, in: Proceedings of the 23$^rd$ Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD ’19), Macau, China, 2019, pp. 280–292.'
date_created: 2023-08-04T07:47:20Z
date_updated: 2023-10-16T13:30:10Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 280–292
place: Macau, China
publication: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD ’19)
status: public
title: Customer Segmentation Based on Transactional Data Using Stream Clustering
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46342'
abstract:
- lang: eng
text: There is a range of phenomena in continuous, global multi-objective optimization,
that cannot occur in single-objective optimization. For instance, in some multi-objective
optimization problems it is possible to follow continuous paths of gradients of
straightforward weighted scalarization functions, starting from locally efficient
solutions, in order to reach globally Pareto optimal solutions. This paper seeks
to better characterize multimodal multi-objective landscapes and to better understand
the transitions from local optima to global optima in simple, path-oriented search
procedures.
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Michael T M
full_name: Emmerich, Michael T M
last_name: Emmerich
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André H
full_name: Deutz, André H
last_name: Deutz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Grimme C, Kerschke P, Emmerich MTM, Preuss M, Deutz AH, Trautmann H. Sliding
to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective
Optimization. In: AIP Conference Proceedings. AIP Publishing; 2019:020052-1-020052-020054.
doi:10.1063/1.5090019'
apa: 'Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., &
Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global
Optima in Multimodal Multi-Objective Optimization. AIP Conference Proceedings,
020052-1-020052–020054. https://doi.org/10.1063/1.5090019'
bibtex: '@inproceedings{Grimme_Kerschke_Emmerich_Preuss_Deutz_Trautmann_2019, place={Leiden,
The Netherlands}, title={Sliding to the Global Optimum: How to Benefit from Non-Global
Optima in Multimodal Multi-Objective Optimization}, DOI={10.1063/1.5090019},
booktitle={AIP Conference Proceedings}, publisher={AIP Publishing}, author={Grimme,
Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and
Deutz, André H and Trautmann, Heike}, year={2019}, pages={020052-1-020052–4} }'
chicago: 'Grimme, Christian, Pascal Kerschke, Michael T M Emmerich, Mike Preuss,
André H Deutz, and Heike Trautmann. “Sliding to the Global Optimum: How to Benefit
from Non-Global Optima in Multimodal Multi-Objective Optimization.” In AIP
Conference Proceedings, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing,
2019. https://doi.org/10.1063/1.5090019.'
ieee: 'C. Grimme, P. Kerschke, M. T. M. Emmerich, M. Preuss, A. H. Deutz, and H.
Trautmann, “Sliding to the Global Optimum: How to Benefit from Non-Global Optima
in Multimodal Multi-Objective Optimization,” in AIP Conference Proceedings,
2019, pp. 020052-1-020052–4, doi: 10.1063/1.5090019.'
mla: 'Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from
Non-Global Optima in Multimodal Multi-Objective Optimization.” AIP Conference
Proceedings, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:10.1063/1.5090019.'
short: 'C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann,
in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019,
pp. 020052-1-020052–4.'
date_created: 2023-08-04T07:48:15Z
date_updated: 2023-10-16T13:30:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1063/1.5090019
language:
- iso: eng
page: 020052-1-020052-4
place: Leiden, The Netherlands
publication: AIP Conference Proceedings
publisher: AIP Publishing
status: public
title: 'Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal
Multi-Objective Optimization'
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46336'
abstract:
- lang: eng
text: Choosing the best-performing optimizer(s) out of a portfolio of optimization
algorithms is usually a difficult and complex task. It gets even worse, if the
underlying functions are unknown, i.e., so-called black-box problems, and function
evaluations are considered to be expensive. In case of continuous single-objective
optimization problems, exploratory landscape analysis (ELA), a sophisticated and
effective approach for characterizing the landscapes of such problems by means
of numerical values before actually performing the optimization task itself, is
advantageous. Unfortunately, until now it has been quite complicated to compute
multiple ELA features simultaneously, as the corresponding code has been—if at
all—spread across multiple platforms or at least across several packages within
these platforms. This article presents a broad summary of existing ELA approaches
and introduces flacco, an R-package for feature-based landscape analysis of continuous
and constrained optimization problems. Although its functions neither solve the
optimization problem itself nor the related algorithm selection problem (ASP),
it offers easy access to an essential ingredient of the ASP by providing a wide
collection of ELA features on a single platform—even within a single package.
In addition, flacco provides multiple visualization techniques, which enhance
the understanding of some of these numerical features, and thereby make certain
landscape properties more comprehensible. On top of that, we will introduce the
package’s built-in, as well as web-hosted and hence platform-independent, graphical
user interface (GUI). It facilitates the usage of the package—especially for people
who are not familiar with R—and thus makes flacco a very convenient toolbox when
working towards algorithm selection of continuous single-objective optimization
problems.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Trautmann H. Comprehensive Feature-Based Landscape Analysis of
Continuous and Constrained Optimization Problems Using the R-package flacco. In:
Bauer N, Ickstadt K, Lübke K, Szepannek G, Trautmann H, Vichi M, eds. Applications
in Statistical Computing. Springer; 2019:93–123. doi:10.1007/978-3-030-25147-5_7'
apa: Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-package
flacco. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, &
M. Vichi (Eds.), Applications in Statistical Computing (pp. 93–123). Springer.
https://doi.org/10.1007/978-3-030-25147-5_7
bibtex: '@inbook{Kerschke_Trautmann_2019, title={Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-package
flacco}, DOI={10.1007/978-3-030-25147-5_7},
booktitle={Applications in Statistical Computing}, publisher={Springer}, author={Kerschke,
Pascal and Trautmann, Heike}, editor={Bauer, Nadja and Ickstadt, Katja and Lübke,
Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}, year={2019},
pages={93–123} }'
chicago: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-Package
Flacco.” In Applications in Statistical Computing, edited by Nadja Bauer,
Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, and Maurizio Vichi,
93–123. Springer, 2019. https://doi.org/10.1007/978-3-030-25147-5_7.
ieee: P. Kerschke and H. Trautmann, “Comprehensive Feature-Based Landscape Analysis
of Continuous and Constrained Optimization Problems Using the R-package flacco,”
in Applications in Statistical Computing, N. Bauer, K. Ickstadt, K. Lübke,
G. Szepannek, H. Trautmann, and M. Vichi, Eds. Springer, 2019, pp. 93–123.
mla: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems Using the R-Package
Flacco.” Applications in Statistical Computing, edited by Nadja Bauer et
al., Springer, 2019, pp. 93–123, doi:10.1007/978-3-030-25147-5_7.
short: 'P. Kerschke, H. Trautmann, in: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek,
H. Trautmann, M. Vichi (Eds.), Applications in Statistical Computing, Springer,
2019, pp. 93–123.'
date_created: 2023-08-04T07:43:30Z
date_updated: 2023-10-16T13:08:22Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-25147-5_7
editor:
- first_name: Nadja
full_name: Bauer, Nadja
last_name: Bauer
- first_name: Katja
full_name: Ickstadt, Katja
last_name: Ickstadt
- first_name: Karsten
full_name: Lübke, Karsten
last_name: Lübke
- first_name: Gero
full_name: Szepannek, Gero
last_name: Szepannek
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Maurizio
full_name: Vichi, Maurizio
last_name: Vichi
language:
- iso: eng
page: 93–123
publication: Applications in Statistical Computing
publisher: Springer
status: public
title: Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained
Optimization Problems Using the R-package flacco
type: book_chapter
user_id: '15504'
year: '2019'
...