---
_id: '46310'
abstract:
- lang: eng
text: 'Classic automated algorithm selection (AS) for (combinatorial) optimization
problems heavily relies on so-called instance features, i.e., numerical characteristics
of the problem at hand ideally extracted with computationally low-demanding routines.
For the traveling salesperson problem (TSP) a plethora of features have been suggested.
Most of these features are, if at all, only normalized imprecisely raising the
issue of feature values being strongly affected by the instance size. Such artifacts
may have detrimental effects on algorithm selection models. We propose a normalization
for two feature groups which stood out in multiple AS studies on the TSP: (a)
features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships
of the input instance. To this end we theoretically derive minimum and maximum
values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean
graphs. We analyze the differences in feature space between normalized versions
of these features and their unnormalized counterparts. Our empirical investigations
on various TSP benchmark sets point out that the feature scaling succeeds in eliminating
the effect of the instance size. A proof-of-concept AS-study shows promising results:
models trained with normalized features tend to outperform those trained with
the respective vanilla features.'
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- 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: Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the
effects of normalized TSP features for automated algorithm selection. Theoretical
Computer Science. 2023;940:123-145. doi:https://doi.org/10.1016/j.tcs.2022.10.019
apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke,
P. (2023). A study on the effects of normalized TSP features for automated algorithm
selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019
bibtex: '@article{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2023, title={A study
on the effects of normalized TSP features for automated algorithm selection},
volume={940}, DOI={https://doi.org/10.1016/j.tcs.2022.10.019},
journal={Theoretical Computer Science}, author={Heins, Jonathan and Bossek, Jakob
and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal},
year={2023}, pages={123–145} }'
chicago: 'Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann,
and Pascal Kerschke. “A Study on the Effects of Normalized TSP Features for Automated
Algorithm Selection.” Theoretical Computer Science 940 (2023): 123–45.
https://doi.org/10.1016/j.tcs.2022.10.019.'
ieee: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A
study on the effects of normalized TSP features for automated algorithm selection,”
Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi: https://doi.org/10.1016/j.tcs.2022.10.019.'
mla: Heins, Jonathan, et al. “A Study on the Effects of Normalized TSP Features
for Automated Algorithm Selection.” Theoretical Computer Science, vol.
940, 2023, pp. 123–45, doi:https://doi.org/10.1016/j.tcs.2022.10.019.
short: J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical
Computer Science 940 (2023) 123–145.
date_created: 2023-08-04T07:18:38Z
date_updated: 2024-06-10T11:57:21Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.tcs.2022.10.019
intvolume: ' 940'
keyword:
- Feature normalization
- Algorithm selection
- Traveling salesperson problem
language:
- iso: eng
page: 123-145
publication: Theoretical Computer Science
publication_identifier:
issn:
- 0304-3975
status: public
title: A study on the effects of normalized TSP features for automated algorithm selection
type: journal_article
user_id: '15504'
volume: 940
year: '2023'
...
---
_id: '48898'
abstract:
- lang: eng
text: 'Automated Algorithm Configuration (AAC) usually takes a global perspective:
it identifies a parameter configuration for an (optimization) algorithm that maximizes
a performance metric over a set of instances. However, the optimal choice of parameters
strongly depends on the instance at hand and should thus be calculated on a per-instance
basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC)
by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach
that is based on deep neural networks. We apply it to predict configurations for
the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem
(TSP) individually for every single instance. To train our PIAC approach, we create
a large set of 100000 TSP instances with 2000 nodes each \textemdash currently
the largest benchmark set to the best of our knowledge. We compare our approach
to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration
(SMAC). The results show that our PIAC approach outperforms this baseline on both
the newly created instance set and established instance sets.'
author:
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Jeroen
full_name: Rook, Jeroen
last_name: Rook
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Oliver Ludger
full_name: Preuß, Oliver Ludger
id: '102978'
last_name: Preuß
orcid: 0009-0008-9308-2418
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Seiler M, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement
Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE
Symposium Series on Computational Intelligence (SSCI). ; :361-368. doi:10.1109/SSCI52147.2023.10372008'
apa: Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann,
H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration
on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI),
361–368. https://doi.org/10.1109/SSCI52147.2023.10372008
bibtex: '@inproceedings{Seiler_Rook_Heins_Preuß_Bossek_Trautmann, title={Using Reinforcement
Learning for Per-Instance Algorithm Configuration on the TSP}, DOI={10.1109/SSCI52147.2023.10372008},
booktitle={2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Seiler,
Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek,
Jakob and Trautmann, Heike}, pages={361–368} }'
chicago: Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob
Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm
Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence
(SSCI), 361–68, n.d. https://doi.org/10.1109/SSCI52147.2023.10372008.
ieee: 'M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using
Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” in
2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368,
doi: 10.1109/SSCI52147.2023.10372008.'
mla: Seiler, Moritz, et al. “Using Reinforcement Learning for Per-Instance Algorithm
Configuration on the TSP.” 2023 IEEE Symposium Series on Computational Intelligence
(SSCI), pp. 361–68, doi:10.1109/SSCI52147.2023.10372008.
short: 'M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023
IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.'
date_created: 2023-11-14T15:59:01Z
date_updated: 2024-06-10T11:56:58Z
department:
- _id: '819'
doi: 10.1109/SSCI52147.2023.10372008
extern: '1'
language:
- iso: eng
page: 361 - 368
publication: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: accepted
status: public
title: Using Reinforcement Learning for Per-Instance Algorithm Configuration on the
TSP
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '46300'
author:
- first_name: Marco
full_name: Niemann, Marco
last_name: Niemann
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Jens
full_name: Brunk, Jens
last_name: Brunk
- first_name: Dennis Maximilian
full_name: Riehle, Dennis Maximilian
last_name: Riehle
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Niemann M, Assenmacher D, Brunk J, Riehle DM, Becker J, Trautmann H. (Semi-)Automatische
Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In: Weitzel G, Mündges
S, eds. Hate Speech — Definitionen, Ausprägungen, Lösungen. VS Verlag für
Sozialwissenschaften; 2022:249–274. doi:10.1007/978-3-658-35658-3_13'
apa: Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., & Trautmann,
H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver
Diskurse. In G. Weitzel & S. Mündges (Eds.), Hate Speech — Definitionen,
Ausprägungen, Lösungen (pp. 249–274). VS Verlag für Sozialwissenschaften.
https://doi.org/10.1007/978-3-658-35658-3_13
bibtex: '@inbook{Niemann_Assenmacher_Brunk_Riehle_Becker_Trautmann_2022, place={Wiesbaden},
title={(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse},
DOI={10.1007/978-3-658-35658-3_13},
booktitle={Hate Speech — Definitionen, Ausprägungen, Lösungen}, publisher={VS
Verlag für Sozialwissenschaften}, author={Niemann, Marco and Assenmacher, Dennis
and Brunk, Jens and Riehle, Dennis Maximilian and Becker, Jörg and Trautmann,
Heike}, editor={Weitzel, Gerrit and Mündges, Stephan}, year={2022}, pages={249–274}
}'
chicago: 'Niemann, Marco, Dennis Assenmacher, Jens Brunk, Dennis Maximilian Riehle,
Jörg Becker, and Heike Trautmann. “(Semi-)Automatische Kommentarmoderation Zur
Erhaltung Konstruktiver Diskurse.” In Hate Speech — Definitionen, Ausprägungen,
Lösungen, edited by Gerrit Weitzel and Stephan Mündges, 249–274. Wiesbaden:
VS Verlag für Sozialwissenschaften, 2022. https://doi.org/10.1007/978-3-658-35658-3_13.'
ieee: 'M. Niemann, D. Assenmacher, J. Brunk, D. M. Riehle, J. Becker, and H. Trautmann,
“(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse,”
in Hate Speech — Definitionen, Ausprägungen, Lösungen, G. Weitzel and S.
Mündges, Eds. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022, pp. 249–274.'
mla: Niemann, Marco, et al. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung
Konstruktiver Diskurse.” Hate Speech — Definitionen, Ausprägungen, Lösungen,
edited by Gerrit Weitzel and Stephan Mündges, VS Verlag für Sozialwissenschaften,
2022, pp. 249–274, doi:10.1007/978-3-658-35658-3_13.
short: 'M. Niemann, D. Assenmacher, J. Brunk, D.M. Riehle, J. Becker, H. Trautmann,
in: G. Weitzel, S. Mündges (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen,
VS Verlag für Sozialwissenschaften, Wiesbaden, 2022, pp. 249–274.'
date_created: 2023-08-04T07:03:47Z
date_updated: 2023-10-16T12:35:41Z
department:
- _id: '819'
- _id: '34'
doi: 10.1007/978-3-658-35658-3_13
editor:
- first_name: Gerrit
full_name: Weitzel, Gerrit
last_name: Weitzel
- first_name: Stephan
full_name: Mündges, Stephan
last_name: Mündges
language:
- iso: eng
page: 249–274
place: Wiesbaden
publication: Hate Speech — Definitionen, Ausprägungen, Lösungen
publication_identifier:
isbn:
- 978-3-658-35658-3
publisher: VS Verlag für Sozialwissenschaften
status: public
title: (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse
type: book_chapter
user_id: '15504'
year: '2022'
...
---
_id: '46301'
author:
- first_name: D
full_name: Assenmacher, D
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Assenmacher D, Trautmann H. Textual One-Pass Stream Clustering with Automated
Distance Threshold Adaption. In: et al. Tran T, ed. Intelligent Information
and Database Systems. Springer International Publishing; 2022:3–16. doi:10.1007/978-3-031-21743-2_1'
apa: Assenmacher, D., & Trautmann, H. (2022). Textual One-Pass Stream Clustering
with Automated Distance Threshold Adaption. In T. et al. Tran (Ed.), Intelligent
Information and Database Systems (pp. 3–16). Springer International Publishing.
https://doi.org/10.1007/978-3-031-21743-2_1
bibtex: '@inproceedings{Assenmacher_Trautmann_2022, place={Cham}, title={Textual
One-Pass Stream Clustering with Automated Distance Threshold Adaption}, DOI={10.1007/978-3-031-21743-2_1},
booktitle={Intelligent Information and Database Systems}, publisher={Springer
International Publishing}, author={Assenmacher, D and Trautmann, Heike}, editor={et
al. Tran, T}, year={2022}, pages={3–16} }'
chicago: 'Assenmacher, D, and Heike Trautmann. “Textual One-Pass Stream Clustering
with Automated Distance Threshold Adaption.” In Intelligent Information and
Database Systems, edited by T et al. Tran, 3–16. Cham: Springer International
Publishing, 2022. https://doi.org/10.1007/978-3-031-21743-2_1.'
ieee: 'D. Assenmacher and H. Trautmann, “Textual One-Pass Stream Clustering with
Automated Distance Threshold Adaption,” in Intelligent Information and Database
Systems, 2022, pp. 3–16, doi: 10.1007/978-3-031-21743-2_1.'
mla: Assenmacher, D., and Heike Trautmann. “Textual One-Pass Stream Clustering with
Automated Distance Threshold Adaption.” Intelligent Information and Database
Systems, edited by T et al. Tran, Springer International Publishing, 2022,
pp. 3–16, doi:10.1007/978-3-031-21743-2_1.
short: 'D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information
and Database Systems, Springer International Publishing, Cham, 2022, pp. 3–16.'
date_created: 2023-08-04T07:04:54Z
date_updated: 2023-10-16T12:35:22Z
department:
- _id: '819'
- _id: '34'
doi: 10.1007/978-3-031-21743-2_1
editor:
- first_name: T
full_name: et al. Tran, T
last_name: et al. Tran
language:
- iso: eng
page: 3–16
place: Cham
publication: Intelligent Information and Database Systems
publisher: Springer International Publishing
status: public
title: Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46316'
abstract:
- lang: eng
text: ' Computational social science uses computational and statistical methods
in order to evaluate social interaction. The public availability of data sets
is thus a necessary precondition for reliable and replicable research. These data
allow researchers to benchmark the computational methods they develop, test the
generalizability of their findings, and build confidence in their results. When
social media data are concerned, data sharing is often restricted for legal or
privacy reasons, which makes the comparison of methods and the replicability of
research results infeasible. Social media analytics research, consequently, faces
an integrity crisis. How is it possible to create trust in computational or statistical
analyses, when they cannot be validated by third parties? In this work, we explore
this well-known, yet little discussed, problem for social media analytics. We
investigate how this problem can be solved by looking at related computational
research areas. Moreover, we propose and implement a prototype to address the
problem in the form of a new evaluation framework that enables the comparison
of algorithms without the need to exchange data directly, while maintaining flexibility
for the algorithm design. '
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Derek
full_name: Weber, Derek
last_name: Weber
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: André Calero
full_name: Valdez, André Calero
last_name: Valdez
- first_name: Alison
full_name: Bradshaw, Alison
last_name: Bradshaw
- first_name: Björn
full_name: Ross, Björn
last_name: Ross
- first_name: Stefano
full_name: Cresci, Stefano
last_name: Cresci
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Assenmacher D, Weber D, Preuss M, et al. Benchmarking Crisis in Social Media
Analytics: A Solution for the Data-Sharing Problem. Social Science Computer
Review. 2022;40(6):1496-1522. doi:10.1177/08944393211012268'
apa: 'Assenmacher, D., Weber, D., Preuss, M., Valdez, A. C., Bradshaw, A., Ross,
B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2022). Benchmarking
Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social
Science Computer Review, 40(6), 1496–1522. https://doi.org/10.1177/08944393211012268'
bibtex: '@article{Assenmacher_Weber_Preuss_Valdez_Bradshaw_Ross_Cresci_Trautmann_Neumann_Grimme_2022,
title={Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing
Problem}, volume={40}, DOI={10.1177/08944393211012268},
number={6}, journal={Social Science Computer Review}, author={Assenmacher, Dennis
and Weber, Derek and Preuss, Mike and Valdez, André Calero and Bradshaw, Alison
and Ross, Björn and Cresci, Stefano and Trautmann, Heike and Neumann, Frank and
Grimme, Christian}, year={2022}, pages={1496–1522} }'
chicago: 'Assenmacher, Dennis, Derek Weber, Mike Preuss, André Calero Valdez, Alison
Bradshaw, Björn Ross, Stefano Cresci, Heike Trautmann, Frank Neumann, and Christian
Grimme. “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing
Problem.” Social Science Computer Review 40, no. 6 (2022): 1496–1522. https://doi.org/10.1177/08944393211012268.'
ieee: 'D. Assenmacher et al., “Benchmarking Crisis in Social Media Analytics:
A Solution for the Data-Sharing Problem,” Social Science Computer Review,
vol. 40, no. 6, pp. 1496–1522, 2022, doi: 10.1177/08944393211012268.'
mla: 'Assenmacher, Dennis, et al. “Benchmarking Crisis in Social Media Analytics:
A Solution for the Data-Sharing Problem.” Social Science Computer Review,
vol. 40, no. 6, 2022, pp. 1496–522, doi:10.1177/08944393211012268.'
short: D. Assenmacher, D. Weber, M. Preuss, A.C. Valdez, A. Bradshaw, B. Ross, S.
Cresci, H. Trautmann, F. Neumann, C. Grimme, Social Science Computer Review 40
(2022) 1496–1522.
date_created: 2023-08-04T07:26:36Z
date_updated: 2023-10-16T12:57:24Z
department:
- _id: '34'
- _id: '819'
doi: 10.1177/08944393211012268
intvolume: ' 40'
issue: '6'
language:
- iso: eng
page: 1496-1522
publication: Social Science Computer Review
status: public
title: 'Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing
Problem'
type: journal_article
user_id: '15504'
volume: 40
year: '2022'
...
---
_id: '46306'
abstract:
- lang: eng
text: Hyperparameter optimization (HPO) is a key component of machine learning models
for achieving peak predictive performance. While numerous methods and algorithms
for HPO have been proposed over the last years, little progress has been made
in illuminating and examining the actual structure of these black-box optimization
problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that
can be used to gain knowledge about properties of unknown optimization problems.
In this paper, we evaluate the performance of five different black-box optimizers
on 30 HPO problems, which consist of two-, three- and five-dimensional continuous
search spaces of the XGBoost learner trained on 10 different data sets. This is
contrasted with the performance of the same optimizers evaluated on 360 problem
instances from the black-box optimization benchmark (BBOB). We then compute ELA
features on the HPO and BBOB problems and examine similarities and differences.
A cluster analysis of the HPO and BBOB problems in ELA feature space allows us
to identify how the HPO problems compare to the BBOB problems on a structural
meta-level. We identify a subset of BBOB problems that are close to the HPO problems
in ELA feature space and show that optimizer performance is comparably similar
on these two sets of benchmark problems. We highlight open challenges of ELA for
HPO and discuss potential directions of future research and applications.
author:
- first_name: Lennart
full_name: Schneider, Lennart
last_name: Schneider
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- 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: 'Schneider L, Schäpermeier L, Prager RP, Bischl B, Trautmann H, Kerschke P.
HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory
Landscape Analysis. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G,
Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer
International Publishing; 2022:575–589. doi:10.1007/978-3-031-14714-2_40'
apa: 'Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H.,
& Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization
Landscapes by Means of Exploratory Landscape Analysis. In G. Rudolph, A. V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem
Solving from Nature — PPSN XVII (pp. 575–589). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_40'
bibtex: '@inproceedings{Schneider_Schäpermeier_Prager_Bischl_Trautmann_Kerschke_2022,
place={Cham}, title={HPO x ELA: Investigating Hyperparameter Optimization Landscapes
by Means of Exploratory Landscape Analysis}, DOI={10.1007/978-3-031-14714-2_40},
booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer
International Publishing}, author={Schneider, Lennart and Schäpermeier, Lennart
and Prager, Raphael Patrick and Bischl, Bernd and Trautmann, Heike and Kerschke,
Pascal}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and
Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}, year={2022}, pages={575–589}
}'
chicago: 'Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd
Bischl, Heike Trautmann, and Pascal Kerschke. “HPO x ELA: Investigating Hyperparameter
Optimization Landscapes by Means of Exploratory Landscape Analysis.” In Parallel
Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V.
Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 575–589.
Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_40.'
ieee: 'L. Schneider, L. Schäpermeier, R. P. Prager, B. Bischl, H. Trautmann, and
P. Kerschke, “HPO x ELA: Investigating Hyperparameter Optimization Landscapes
by Means of Exploratory Landscape Analysis,” in Parallel Problem Solving from
Nature — PPSN XVII, 2022, pp. 575–589, doi: 10.1007/978-3-031-14714-2_40.'
mla: 'Schneider, Lennart, et al. “HPO x ELA: Investigating Hyperparameter Optimization
Landscapes by Means of Exploratory Landscape Analysis.” Parallel Problem Solving
from Nature — PPSN XVII, edited by Günter Rudolph et al., Springer International
Publishing, 2022, pp. 575–589, doi:10.1007/978-3-031-14714-2_40.'
short: 'L. Schneider, L. Schäpermeier, R.P. Prager, B. Bischl, H. Trautmann, P.
Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T.
Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International
Publishing, Cham, 2022, pp. 575–589.'
date_created: 2023-08-04T07:15:16Z
date_updated: 2023-10-16T12:51:27Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-031-14714-2_40
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tušar, Tea
last_name: Tušar
language:
- iso: eng
page: 575–589
place: Cham
publication: Parallel Problem Solving from Nature — PPSN XVII
publication_identifier:
isbn:
- 978-3-031-14714-2
publisher: Springer International Publishing
status: public
title: 'HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of
Exploratory Landscape Analysis'
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46308'
abstract:
- lang: eng
text: Single-objective continuous optimization can be challenging, especially when
dealing with multimodal problems. This work sheds light on the effects that multi-objective
optimization may have in the single-objective space. For this purpose, we examine
the inner mechanisms of the recently developed sophisticated local search procedure
SOMOGSA. This method solves multimodal single-objective continuous optimization
problems based on first expanding the problem with an additional objective (e.g.,
a sphere function) to the bi-objective domain and subsequently exploiting local
structures of the resulting landscapes. Our study particularly focuses on the
sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization
of the artificial second objective, as well as (2) the position of the initial
starting points in the search space. As SOMOGSA is a modular framework for encapsulating
local search, we integrate Nelder–Mead local search as optimizer in the respective
module and compare the performance of the resulting hybrid local search to its
original single-objective counterpart. We show that the SOMOGSA framework can
significantly boost local search by multiobjectivization. Hence, combined with
more sophisticated local search and metaheuristics, this may help solve highly
multimodal optimization problems in the future.
author:
- first_name: Pelin
full_name: Aspar, Pelin
last_name: Aspar
- first_name: Vera
full_name: Steinhoff, Vera
last_name: Steinhoff
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- 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
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Aspar P, Steinhoff V, Schäpermeier L, Kerschke P, Trautmann H, Grimme C. The
objective that freed me: a multi-objective local search approach for continuous
single-objective optimization. Natural Computing. 2022;1:1–15. doi:10.1007/s11047-022-09919-w'
apa: 'Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., &
Grimme, C. (2022). The objective that freed me: a multi-objective local search
approach for continuous single-objective optimization. Natural Computing,
1, 1–15. https://doi.org/10.1007/s11047-022-09919-w'
bibtex: '@article{Aspar_Steinhoff_Schäpermeier_Kerschke_Trautmann_Grimme_2022, title={The
objective that freed me: a multi-objective local search approach for continuous
single-objective optimization}, volume={1}, DOI={10.1007/s11047-022-09919-w},
journal={Natural Computing}, author={Aspar, Pelin and Steinhoff, Vera and Schäpermeier,
Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}, year={2022},
pages={1–15} }'
chicago: 'Aspar, Pelin, Vera Steinhoff, Lennart Schäpermeier, Pascal Kerschke, Heike
Trautmann, and Christian Grimme. “The Objective That Freed Me: A Multi-Objective
Local Search Approach for Continuous Single-Objective Optimization.” Natural
Computing 1 (2022): 1–15. https://doi.org/10.1007/s11047-022-09919-w.'
ieee: 'P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, and C.
Grimme, “The objective that freed me: a multi-objective local search approach
for continuous single-objective optimization,” Natural Computing, vol.
1, pp. 1–15, 2022, doi: 10.1007/s11047-022-09919-w.'
mla: 'Aspar, Pelin, et al. “The Objective That Freed Me: A Multi-Objective Local
Search Approach for Continuous Single-Objective Optimization.” Natural Computing,
vol. 1, 2022, pp. 1–15, doi:10.1007/s11047-022-09919-w.'
short: P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, C. Grimme,
Natural Computing 1 (2022) 1–15.
date_created: 2023-08-04T07:16:40Z
date_updated: 2023-10-16T12:52:33Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/s11047-022-09919-w
intvolume: ' 1'
language:
- iso: eng
page: 1–15
publication: Natural Computing
status: public
title: 'The objective that freed me: a multi-objective local search approach for continuous
single-objective optimization'
type: journal_article
user_id: '15504'
volume: 1
year: '2022'
...
---
_id: '48861'
abstract:
- lang: eng
text: Generating instances of different properties is key to algorithm selection
methods that differentiate between the performance of different solvers for a
given combinatorial optimization problem. A wide range of methods using evolutionary
computation techniques has been introduced in recent years. With this paper, we
contribute to this area of research by providing a new approach based on quality
diversity (QD) that is able to explore the whole feature space. QD algorithms
allow to create solutions of high quality within a given feature space by splitting
it up into boxes and improving solution quality within each box. We use our QD
approach for the generation of TSP instances to visualize and analyze the variety
of instances differentiating various TSP solvers and compare it to instances generated
by established approaches from the literature.
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
citation:
ama: 'Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality
Diversity. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:10.1145/3512290.3528851'
apa: Bossek, J., & Neumann, F. (2022). Exploring the Feature Space of TSP Instances
Using Quality Diversity. Proceedings of the Genetic and Evolutionary Computation
Conference, 186–194. https://doi.org/10.1145/3512290.3528851
bibtex: '@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO
’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity},
DOI={10.1145/3512290.3528851},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }'
chicago: 'Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP
Instances Using Quality Diversity.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 186–194. GECCO ’22. New York, NY, USA: Association
for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528851.'
ieee: 'J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using
Quality Diversity,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2022, pp. 186–194, doi: 10.1145/3512290.3528851.'
mla: Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances
Using Quality Diversity.” Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, 2022, pp. 186–194, doi:10.1145/3512290.3528851.
short: 'J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp.
186–194.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:56Z
department:
- _id: '819'
doi: 10.1145/3512290.3528851
extern: '1'
keyword:
- instance features
- instance generation
- quality diversity
- TSP
language:
- iso: eng
page: 186–194
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-9237-2
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’22
status: public
title: Exploring the Feature Space of TSP Instances Using Quality Diversity
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48868'
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. Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA. In: Proceedings of the Genetic
and Evolutionary Computation Conference Companion. GECCO’22. Association for
Computing Machinery; 2022:824–842. doi:10.1145/3520304.3533626'
apa: 'Bossek, J., Neumann, A., & Neumann, F. (2022). Evolutionary Diversity
Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.
Proceedings of the Genetic and Evolutionary Computation Conference Companion,
824–842. https://doi.org/10.1145/3520304.3533626'
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2022, place={New York, NY, USA},
series={GECCO’22}, title={Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA}, DOI={10.1145/3520304.3533626},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob
and Neumann, Aneta and Neumann, Frank}, year={2022}, pages={824–842}, collection={GECCO’22}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Evolutionary Diversity
Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.”
In Proceedings of the Genetic and Evolutionary Computation Conference Companion,
824–842. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022.
https://doi.org/10.1145/3520304.3533626.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization
for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 2022, pp.
824–842, doi: 10.1145/3520304.3533626.'
mla: 'Bossek, Jakob, et al. “Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA.” Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing
Machinery, 2022, pp. 824–842, doi:10.1145/3520304.3533626.'
short: 'J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary
Computation Conference Companion, Association for Computing Machinery, New York,
NY, USA, 2022, pp. 824–842.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:19Z
department:
- _id: '819'
doi: 10.1145/3520304.3533626
extern: '1'
language:
- iso: eng
page: 824–842
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-9268-6
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO’22
status: public
title: 'Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial
at GECCO’22, Boston, USA'
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48882'
abstract:
- lang: eng
text: In multimodal multi-objective optimization (MMMOO), the focus is not solely
on convergence in objective space, but rather also on explicitly ensuring diversity
in decision space. We illustrate why commonly used diversity measures are not
entirely appropriate for this task and propose a sophisticated basin-based evaluation
(BBE) method. Also, BBE variants are developed, capturing the anytime behavior
of algorithms. The set of BBE measures is tested by means of an algorithm configuration
study. We show that these new measures also transfer properties of the well-established
hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective
space convergence. Moreover, we advance MMMOO research by providing insights into
the multimodal performance of the considered algorithms. Specifically, algorithms
exploiting local structures are shown to outperform classical evolutionary multi-objective
optimizers regarding the BBE variants and respective trade-off with HV.
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jeroen
full_name: Rook, Jeroen
last_name: Rook
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based
Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G,
Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tusar T, eds. Parallel Problem
Solving from Nature (PPSN XVII). Lecture Notes in Computer Science. Springer
International Publishing; 2022:192–206. doi:10.1007/978-3-031-14714-2_14'
apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann,
H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization
Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &
T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp.
192–206). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_14'
bibtex: '@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022,
place={Cham}, series={Lecture Notes in Computer Science}, title={BBE: Basin-Based
Evaluation of Multimodal Multi-objective Optimization Problems}, DOI={10.1007/978-3-031-14714-2_14},
booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer
International Publishing}, author={Heins, Jonathan and Rook, Jeroen and Schäpermeier,
Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph,
Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa,
Gabriela and Tusar, Tea}, year={2022}, pages={192–206}, collection={Lecture Notes
in Computer Science} }'
chicago: 'Heins, Jonathan, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob
Bossek, and Heike Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective
Optimization Problems.” In Parallel Problem Solving from Nature (PPSN XVII),
edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela
Ochoa, and Tea Tusar, 192–206. Lecture Notes in Computer Science. Cham: Springer
International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_14.'
ieee: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann,
“BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,”
in Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 192–206,
doi: 10.1007/978-3-031-14714-2_14.'
mla: 'Heins, Jonathan, et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective
Optimization Problems.” Parallel Problem Solving from Nature (PPSN XVII),
edited by Günter Rudolph et al., Springer International Publishing, 2022, pp.
192–206, doi:10.1007/978-3-031-14714-2_14.'
short: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann,
in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.),
Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing,
Cham, 2022, pp. 192–206.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:50Z
department:
- _id: '819'
doi: 10.1007/978-3-031-14714-2_14
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
extern: '1'
keyword:
- Anytime behavior
- Benchmarking
- Continuous optimization
- Multi-objective optimization
- Multimodality
- Performance metric
language:
- iso: eng
page: 192–206
place: Cham
publication: Parallel Problem Solving from Nature (PPSN XVII)
publication_identifier:
isbn:
- 978-3-031-14714-2
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems'
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48894'
abstract:
- lang: eng
text: Recently different evolutionary computation approaches have been developed
that generate sets of high quality diverse solutions for a given optimisation
problem. Many studies have considered diversity 1) as a mean to explore niches
in behavioural space (quality diversity) or 2) to increase the structural differences
of solutions (evolutionary diversity optimisation). In this study, we introduce
a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component
traveling thief problem. The results show the capability of the co-evolutionary
algorithm to achieve significantly higher diversity compared to the baseline evolutionary
diversity algorithms from the literature.
author:
- first_name: Adel
full_name: Nikfarjam, Adel
last_name: Nikfarjam
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- 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: 'Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation
for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke
P, Ochoa G, Tu\v sar T, eds. Parallel Problem Solving from Nature (PPSN XVII).
Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249.
doi:10.1007/978-3-031-14714-2_17'
apa: Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-Evolutionary
Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, & T. Tu\v sar (Eds.), Parallel Problem
Solving from Nature (PPSN XVII) (pp. 237–249). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_17
bibtex: '@inproceedings{Nikfarjam_Neumann_Bossek_Neumann_2022, place={Cham}, series={Lecture
Notes in Computer Science}, title={Co-Evolutionary Diversity Optimisation for
the Traveling Thief Problem}, DOI={10.1007/978-3-031-14714-2_17},
booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer
International Publishing}, author={Nikfarjam, Adel and Neumann, Aneta and Bossek,
Jakob and Neumann, Frank}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre,
Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}, year={2022},
pages={237–249}, collection={Lecture Notes in Computer Science} }'
chicago: 'Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary
Diversity Optimisation for the Traveling Thief Problem.” In Parallel Problem
Solving from Nature (PPSN XVII), edited by Günter Rudolph, Anna V. Kononova,
Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tu\v sar, 237–249. Lecture
Notes in Computer Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_17.'
ieee: 'A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity
Optimisation for the Traveling Thief Problem,” in Parallel Problem Solving
from Nature (PPSN XVII), 2022, pp. 237–249, doi: 10.1007/978-3-031-14714-2_17.'
mla: Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling
Thief Problem.” Parallel Problem Solving from Nature (PPSN XVII), edited
by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249,
doi:10.1007/978-3-031-14714-2_17.
short: 'A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\v sar (Eds.), Parallel Problem Solving
from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:51Z
department:
- _id: '819'
doi: 10.1007/978-3-031-14714-2_17
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tu\v sar, Tea
last_name: Tu\v sar
extern: '1'
keyword:
- Co-evolutionary algorithms
- Evolutionary diversity optimisation
- Quality diversity
- Traveling thief problem
language:
- iso: eng
page: 237–249
place: Cham
publication: Parallel Problem Solving from Nature (PPSN XVII)
publication_identifier:
isbn:
- 978-3-031-14714-2
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48878'
abstract:
- lang: eng
text: Due to the rise of continuous data-generating applications, analyzing data
streams has gained increasing attention over the past decades. A core research
area in stream data is stream classification, which categorizes or detects data
points within an evolving stream of observations. Areas of stream classification
are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing
a wide range of (social) media applications. Research in stream classification
is related to developing methods that adapt to the changing and potentially volatile
data stream. It focuses on individual aspects of the stream classification pipeline,
e.g., designing suitable algorithm architectures, an efficient train and test
procedure, or detecting so-called concept drifts. As a result of the many different
research questions and strands, the field is challenging to grasp, especially
for beginners. This survey explores, summarizes, and categorizes work within the
domain of stream classification and identifies core research threads over the
past few years. It is structured based on the stream classification process to
facilitate coordination within this complex topic, including common application
scenarios and benchmarking data sets. Thus, both newcomers to the field and experts
who want to widen their scope can gain (additional) insight into this research
area and find starting points and pointers to more in-depth literature on specific
issues and research directions in the field.
author:
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
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: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(18):9094.
doi:10.3390/app12189094'
apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review. Applied
Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094'
bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094},
number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital
Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek,
Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }'
chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.'
ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
Stream Classification Pipeline: A Literature Review,” Applied Sciences,
vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.'
mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital
Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.'
short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
12 (2022) 9094.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:50:56Z
department:
- _id: '819'
doi: 10.3390/app12189094
intvolume: ' 12'
issue: '18'
keyword:
- big data
- data mining
- data stream analysis
- machine learning
- stream classification
- supervised learning
language:
- iso: eng
page: '9094'
publication: Applied Sciences
publication_identifier:
issn:
- 2076-3417
publisher: '{Multidisciplinary Digital Publishing Institute}'
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '102979'
volume: 12
year: '2022'
...
---
_id: '48896'
abstract:
- lang: eng
text: Hardness of Multi-Objective (MO) continuous optimization problems results
from an interplay of various problem characteristics, e. g. the degree of multi-modality.
We present a benchmark study of classical and diversity focused optimizers on
multi-modal MO problems based on automated algorithm configuration. We show the
large effect of the latter and investigate the trade-off between convergence in
objective space and diversity in decision space.
author:
- first_name: Jeroen
full_name: Rook, Jeroen
last_name: Rook
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- 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
citation:
ama: 'Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm
Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Proceedings
of the Genetic and Evolutionary Computation Conference Companion. GECCO’22.
Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998'
apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential
of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization
Problems. Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 356–359. https://doi.org/10.1145/3520304.3528998
bibtex: '@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY,
USA}, series={GECCO’22}, title={On the Potential of Automated Algorithm Configuration
on Multi-Modal Multi-Objective Optimization Problems}, DOI={10.1145/3520304.3528998},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Rook, Jeroen
and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}, year={2022}, pages={356–359},
collection={GECCO’22} }'
chicago: 'Rook, Jeroen, Heike Trautmann, Jakob Bossek, and Christian Grimme. “On
the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimization Problems.” In Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 356–359. GECCO’22. New York, NY, USA: Association for
Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.'
ieee: 'J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated
Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,”
in Proceedings of the Genetic and Evolutionary Computation Conference Companion,
2022, pp. 356–359, doi: 10.1145/3520304.3528998.'
mla: Rook, Jeroen, et al. “On the Potential of Automated Algorithm Configuration
on Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing
Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998.
short: 'J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing Machinery,
New York, NY, USA, 2022, pp. 356–359.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:50:24Z
department:
- _id: '819'
doi: 10.1145/3520304.3528998
extern: '1'
keyword:
- configuration
- multi-modality
- multi-objective optimization
language:
- iso: eng
page: 356–359
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-9268-6
publisher: Association for Computing Machinery
series_title: GECCO’22
status: public
title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimization Problems
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '52532'
author:
- first_name: Agatha S.
full_name: Rodrigues, Agatha S.
last_name: Rodrigues
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Carlos Alberto De Bragança
full_name: Pereira, Carlos Alberto De Bragança
last_name: Pereira
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Carolin
full_name: Wagner, Carolin
last_name: Wagner
- first_name: Bernd
full_name: Hellingrath, Bernd
last_name: Hellingrath
- first_name: Adriano
full_name: Polpo, Adriano
last_name: Polpo
citation:
ama: Rodrigues AS, Kerschke P, Pereira CADB, et al. Estimation of component reliability
from superposed renewal processes by means of latent variables. Comput Stat.
2022;37(1):355–379. doi:10.1007/S00180-021-01124-0
apa: Rodrigues, A. S., Kerschke, P., Pereira, C. A. D. B., Trautmann, H., Wagner,
C., Hellingrath, B., & Polpo, A. (2022). Estimation of component reliability
from superposed renewal processes by means of latent variables. Comput. Stat.,
37(1), 355–379. https://doi.org/10.1007/S00180-021-01124-0
bibtex: '@article{Rodrigues_Kerschke_Pereira_Trautmann_Wagner_Hellingrath_Polpo_2022,
title={Estimation of component reliability from superposed renewal processes by
means of latent variables}, volume={37}, DOI={10.1007/S00180-021-01124-0},
number={1}, journal={Comput. Stat.}, author={Rodrigues, Agatha S. and Kerschke,
Pascal and Pereira, Carlos Alberto De Bragança and Trautmann, Heike and Wagner,
Carolin and Hellingrath, Bernd and Polpo, Adriano}, year={2022}, pages={355–379}
}'
chicago: 'Rodrigues, Agatha S., Pascal Kerschke, Carlos Alberto De Bragança Pereira,
Heike Trautmann, Carolin Wagner, Bernd Hellingrath, and Adriano Polpo. “Estimation
of Component Reliability from Superposed Renewal Processes by Means of Latent
Variables.” Comput. Stat. 37, no. 1 (2022): 355–379. https://doi.org/10.1007/S00180-021-01124-0.'
ieee: 'A. S. Rodrigues et al., “Estimation of component reliability from
superposed renewal processes by means of latent variables,” Comput. Stat.,
vol. 37, no. 1, pp. 355–379, 2022, doi: 10.1007/S00180-021-01124-0.'
mla: Rodrigues, Agatha S., et al. “Estimation of Component Reliability from Superposed
Renewal Processes by Means of Latent Variables.” Comput. Stat., vol. 37,
no. 1, 2022, pp. 355–379, doi:10.1007/S00180-021-01124-0.
short: A.S. Rodrigues, P. Kerschke, C.A.D.B. Pereira, H. Trautmann, C. Wagner, B.
Hellingrath, A. Polpo, Comput. Stat. 37 (2022) 355–379.
date_created: 2024-03-13T09:59:21Z
date_updated: 2024-03-13T10:28:01Z
department:
- _id: '819'
doi: 10.1007/S00180-021-01124-0
intvolume: ' 37'
issue: '1'
language:
- iso: eng
page: 355–379
publication: Comput. Stat.
status: public
title: Estimation of component reliability from superposed renewal processes by means
of latent variables
type: journal_article
user_id: '15504'
volume: 37
year: '2022'
...
---
_id: '46307'
abstract:
- lang: eng
text: Exploratory Landscape Analysis is a powerful technique for numerically characterizing
landscapes of single-objective continuous optimization problems. Landscape insights
are crucial both for problem understanding as well as for assessing benchmark
set diversity and composition. Despite the irrefutable usefulness of these features,
they suffer from their own ailments and downsides. Hence, in this work we provide
a collection of different approaches to characterize optimization landscapes.
Similar to conventional landscape features, we require a small initial sample.
However, instead of computing features based on that sample, we develop alternative
representations of the original sample. These range from point clouds to 2D images
and, therefore, are entirely feature-free. We demonstrate and validate our devised
methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level,
expert-based landscape properties such as the degree of multimodality and the
existence of funnel structures. The quality of our approaches is on par with methods
relying on the traditional landscape features. Thereby, we provide an exciting
new perspective on every research area which utilizes problem information such
as problem understanding and algorithm design as well as automated algorithm configuration
and selection.
author:
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- 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, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based
Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness
Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference.
Association for Computing Machinery; 2022:657–665. doi:10.1145/3512290.3528834'
apa: Seiler, M., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection
of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective
Continuous Fitness Landscapes. Proceedings of the Genetic and Evolutionary
Computation Conference, 657–665. https://doi.org/10.1145/3512290.3528834
bibtex: '@inproceedings{Seiler_Prager_Kerschke_Trautmann_2022, place={New York,
NY, USA}, title={A Collection of Deep Learning-based Feature-Free Approaches for
Characterizing Single-Objective Continuous Fitness Landscapes}, DOI={10.1145/3512290.3528834},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Seiler, Moritz and Prager,
Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={657–665}
}'
chicago: 'Seiler, Moritz, Raphael Patrick Prager, Pascal Kerschke, and Heike Trautmann.
“A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing
Single-Objective Continuous Fitness Landscapes.” In Proceedings of the Genetic
and Evolutionary Computation Conference, 657–665. New York, NY, USA: Association
for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528834.'
ieee: 'M. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “A Collection of
Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective
Continuous Fitness Landscapes,” in Proceedings of the Genetic and Evolutionary
Computation Conference, 2022, pp. 657–665, doi: 10.1145/3512290.3528834.'
mla: Seiler, Moritz, et al. “A Collection of Deep Learning-Based Feature-Free Approaches
for Characterizing Single-Objective Continuous Fitness Landscapes.” Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2022, pp. 657–665, doi:10.1145/3512290.3528834.
short: 'M. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the
Genetic and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2022, pp. 657–665.'
date_created: 2023-08-04T07:15:59Z
date_updated: 2024-06-07T07:13:23Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3512290.3528834
language:
- iso: eng
page: 657–665
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- '9781450392372'
publisher: Association for Computing Machinery
status: public
title: A Collection of Deep Learning-based Feature-Free Approaches for Characterizing
Single-Objective Continuous Fitness Landscapes
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46304'
abstract:
- lang: eng
text: In recent years, feature-based automated algorithm selection using exploratory
landscape analysis has demonstrated its great potential in single-objective continuous
black-box optimization. However, feature computation is problem-specific and can
be costly in terms of computational resources. This paper investigates feature-free
approaches that rely on state-of-the-art deep learning techniques operating on
either images or point clouds. We show that point-cloud-based strategies, in particular,
are highly competitive and also substantially reduce the size of the required
solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive
learning in automated algorithm selection models.
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- 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: 'Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection
in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning
and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke
P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII.
Springer International Publishing; 2022:3–17. doi:10.1007/978-3-031-14714-2_1'
apa: 'Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2022). Automated
Algorithm Selection in Single-Objective Continuous Optimization: A Comparative
Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem
Solving from Nature — PPSN XVII (pp. 3–17). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_1'
bibtex: '@inproceedings{Prager_Seiler_Trautmann_Kerschke_2022, place={Cham}, title={Automated
Algorithm Selection in Single-Objective Continuous Optimization: A Comparative
Study of Deep Learning and Landscape Analysis Methods}, DOI={10.1007/978-3-031-14714-2_1},
booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer
International Publishing}, author={Prager, Raphael Patrick and Seiler, Moritz
and Trautmann, Heike and Kerschke, Pascal}, editor={Rudolph, Günter and Kononova,
Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar,
Tea}, year={2022}, pages={3–17} }'
chicago: 'Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke.
“Automated Algorithm Selection in Single-Objective Continuous Optimization: A
Comparative Study of Deep Learning and Landscape Analysis Methods.” In Parallel
Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V.
Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17.
Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.'
ieee: 'R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm
Selection in Single-Objective Continuous Optimization: A Comparative Study of
Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving
from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.'
mla: 'Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective
Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis
Methods.” Parallel Problem Solving from Nature — PPSN XVII, edited by Günter
Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:10.1007/978-3-031-14714-2_1.'
short: 'R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V.
Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem
Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022,
pp. 3–17.'
date_created: 2023-08-04T07:12:33Z
date_updated: 2024-06-07T07:13:47Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-031-14714-2_1
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tušar, Tea
last_name: Tušar
language:
- iso: eng
page: 3–17
place: Cham
publication: Parallel Problem Solving from Nature — PPSN XVII
publication_identifier:
isbn:
- 978-3-031-14714-2
publisher: Springer International Publishing
status: public
title: 'Automated Algorithm Selection in Single-Objective Continuous Optimization:
A Comparative Study of Deep Learning and Landscape Analysis Methods'
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46303'
abstract:
- lang: eng
text: Social media platforms are essential for information sharing and, thus, prone
to coordinated dis- and misinformation campaigns. Nevertheless, research in this
area is hampered by strict data sharing regulations imposed by the platforms,
resulting in a lack of benchmark data. Previous work focused on circumventing
these rules by either pseudonymizing the data or sharing fragments. In this work,
we will address the benchmarking crisis by presenting a methodology that can be
used to create artificial campaigns out of original campaign building blocks.
We conduct a proof-of-concept study using the freely available generative language
model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly
be adapted to an underlying social media stream and evade state-of-the-art campaign
detection approaches based on stream clustering. Thus, we not only provide a framework
for artificial benchmark generation but also demonstrate the possible adversarial
nature of such benchmarks for challenging and advancing current campaign detection
methods.
author:
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
last_name: Pohl
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- 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: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social
Media Campaign Creation for Benchmarking and Challenging Detection Approaches.
In: the Advancement of Artificial Intelligence (AAAI) Association for, ed. Workshop
Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM).
AAAI Press; 2022:1–10. doi:10.36190/2022.91'
apa: Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., & Grimme, C. (2022).
Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection
Approaches. In for the Advancement of Artificial Intelligence (AAAI) Association
(Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web
and Social Media (ICWSM) (pp. 1–10). AAAI Press. https://doi.org/10.36190/2022.91
bibtex: '@inproceedings{Pohl_Assenmacher_Seiler_Trautmann_Grimme_2022, place={Palo
Alto, CA, USA}, title={Artificial Social Media Campaign Creation for Benchmarking
and Challenging Detection Approaches}, DOI={10.36190/2022.91},
booktitle={Workshop Proceedings of the 16$^th$ International Conference on Web
and Social Media (ICWSM)}, publisher={AAAI Press}, author={Pohl, Janina Susanne
and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian},
editor={the Advancement of Artificial Intelligence (AAAI) Association, for}, year={2022},
pages={1–10} }'
chicago: 'Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann,
and Christian Grimme. “Artificial Social Media Campaign Creation for Benchmarking
and Challenging Detection Approaches.” In Workshop Proceedings of the 16$^th$
International Conference on Web and Social Media (ICWSM), edited by for the
Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA,
USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.'
ieee: 'J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial
Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,”
in Workshop Proceedings of the 16$^th$ International Conference on Web and
Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.'
mla: Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for
Benchmarking and Challenging Detection Approaches.” Workshop Proceedings of
the 16$^th$ International Conference on Web and Social Media (ICWSM), edited
by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press,
2022, pp. 1–10, doi:10.36190/2022.91.
short: 'J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in: for
the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop
Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM),
AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.'
date_created: 2023-08-04T07:11:34Z
date_updated: 2024-06-07T07:13:35Z
department:
- _id: '34'
- _id: '819'
doi: 10.36190/2022.91
editor:
- first_name: for
full_name: the Advancement of Artificial Intelligence (AAAI) Association, for
last_name: the Advancement of Artificial Intelligence (AAAI) Association
language:
- iso: eng
page: 1–10
place: Palo Alto, CA, USA
publication: Workshop Proceedings of the 16$^th$ International Conference on Web and
Social Media (ICWSM)
publisher: AAAI Press
status: public
title: Artificial Social Media Campaign Creation for Benchmarking and Challenging
Detection Approaches
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46309'
abstract:
- lang: eng
text: Due to the rise of continuous data-generating applications, analyzing data
streams has gained increasing attention over the past decades. A core research
area in stream data is stream classification, which categorizes or detects data
points within an evolving stream of observations. Areas of stream classification
are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range
of (social) media applications. Research in stream classification is related to
developing methods that adapt to the changing and potentially volatile data stream.
It focuses on individual aspects of the stream classification pipeline, e.g.,
designing suitable algorithm architectures, an efficient train and test procedure,
or detecting so-called concept drifts. As a result of the many different research
questions and strands, the field is challenging to grasp, especially for beginners.
This survey explores, summarizes, and categorizes work within the domain of stream
classification and identifies core research threads over the past few years. It
is structured based on the stream classification process to facilitate coordination
within this complex topic, including common application scenarios and benchmarking
data sets. Thus, both newcomers to the field and experts who want to widen their
scope can gain (additional) insight into this research area and find starting
points and pointers to more in-depth literature on specific issues and research
directions in the field.
author:
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
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: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(8):1–44.
doi:10.3390/app12189094'
apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review. Applied
Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094'
bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094},
number={8}, journal={Applied Sciences}, author={Clever, Lena and Pohl, Janina
Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022},
pages={1–44} }'
chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences 12, no. 8 (2022): 1–44. https://doi.org/10.3390/app12189094.'
ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
Stream Classification Pipeline: A Literature Review,” Applied Sciences,
vol. 12, no. 8, pp. 1–44, 2022, doi: 10.3390/app12189094.'
mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences, vol. 12, no. 8, 2022, pp. 1–44, doi:10.3390/app12189094.'
short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
12 (2022) 1–44.
date_created: 2023-08-04T07:17:23Z
date_updated: 2024-06-10T12:02:17Z
department:
- _id: '34'
- _id: '819'
doi: 10.3390/app12189094
intvolume: ' 12'
issue: '8'
language:
- iso: eng
page: 1–44
publication: Applied Sciences
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '15504'
volume: 12
year: '2022'
...
---
_id: '46302'
author:
- first_name: J
full_name: Heins, J
last_name: Heins
- first_name: J
full_name: Rook, J
last_name: Rook
- first_name: L
full_name: Schäpermeier, L
last_name: Schäpermeier
- first_name: P
full_name: Kerschke, P
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based
Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G,
Kononova A, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem
Solving from Nature — PPSN XVII. Springer International Publishing; 2022:192–206.'
apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann,
H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization
Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &
T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp.
192–206). Springer International Publishing.'
bibtex: '@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022,
place={Cham}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective
Optimization Problems}, booktitle={Parallel Problem Solving from Nature — PPSN
XVII}, publisher={Springer International Publishing}, author={Heins, J and Rook,
J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike},
editor={Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa,
G and Tušar, T}, year={2022}, pages={192–206} }'
chicago: 'Heins, J, J Rook, L Schäpermeier, P Kerschke, Jakob Bossek, and Heike
Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization
Problems.” In Parallel Problem Solving from Nature — PPSN XVII, edited
by G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, and T Tušar, 192–206.
Cham: Springer International Publishing, 2022.'
ieee: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann,
“BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,”
in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 192–206.'
mla: 'Heins, J., et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective
Optimization Problems.” Parallel Problem Solving from Nature — PPSN XVII,
edited by G Rudolph et al., Springer International Publishing, 2022, pp. 192–206.'
short: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann,
in: G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.),
Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing,
Cham, 2022, pp. 192–206.'
date_created: 2023-08-04T07:10:52Z
date_updated: 2024-06-10T12:02:35Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: AV
full_name: Kononova, AV
last_name: Kononova
- first_name: H
full_name: Aguirre, H
last_name: Aguirre
- first_name: P
full_name: Kerschke, P
last_name: Kerschke
- first_name: G
full_name: Ochoa, G
last_name: Ochoa
- first_name: T
full_name: Tušar, T
last_name: Tušar
language:
- iso: eng
page: 192–206
place: Cham
publication: Parallel Problem Solving from Nature — PPSN XVII
publication_identifier:
isbn:
- 978-3-031-14714-2
publisher: Springer International Publishing
status: public
title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems'
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '46305'
abstract:
- lang: eng
text: Hardness of Multi-Objective (MO) continuous optimization problems results
from an interplay of various problem characteristics, e. g. the degree of multi-modality.
We present a benchmark study of classical and diversity focused optimizers on
multi-modal MO problems based on automated algorithm configuration. We show the
large effect of the latter and investigate the trade-off between convergence in
objective space and diversity in decision space.
author:
- first_name: J
full_name: Rook, J
last_name: Rook
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: C
full_name: Grimme, C
last_name: Grimme
citation:
ama: 'Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm
Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Fieldsend
J, Wagner M, eds. Proceedings of the Genetic and Evolutionary Computation Conference
Companion. GECCO ’22. Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998'
apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential
of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization
Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic
and Evolutionary Computation Conference Companion (pp. 356–359). Association
for Computing Machinery. https://doi.org/10.1145/3520304.3528998
bibtex: '@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY,
USA}, series={GECCO ’22}, title={On the Potential of Automated Algorithm Configuration
on Multi-Modal Multi-Objective Optimization Problems}, DOI={10.1145/3520304.3528998},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Rook, J and
Trautmann, Heike and Bossek, Jakob and Grimme, C}, editor={Fieldsend, J and Wagner,
M.}, year={2022}, pages={356–359}, collection={GECCO ’22} }'
chicago: 'Rook, J, Heike Trautmann, Jakob Bossek, and C Grimme. “On the Potential
of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization
Problems.” In Proceedings of the Genetic and Evolutionary Computation Conference
Companion, edited by J Fieldsend and M. Wagner, 356–359. GECCO ’22. New York,
NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.'
ieee: 'J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated
Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,”
in Proceedings of the Genetic and Evolutionary Computation Conference Companion,
2022, pp. 356–359, doi: 10.1145/3520304.3528998.'
mla: Rook, J., et al. “On the Potential of Automated Algorithm Configuration on
Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic
and Evolutionary Computation Conference Companion, edited by J Fieldsend and
M. Wagner, Association for Computing Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998.
short: 'J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: J. Fieldsend, M. Wagner
(Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion,
Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.'
date_created: 2023-08-04T07:14:24Z
date_updated: 2026-02-19T15:12:35Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3520304.3528998
editor:
- first_name: J
full_name: Fieldsend, J
last_name: Fieldsend
- first_name: M.
full_name: Wagner, M.
last_name: Wagner
language:
- iso: eng
page: 356–359
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- '9781450392686'
publisher: Association for Computing Machinery
series_title: GECCO ’22
status: public
title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimization Problems
type: conference
user_id: '14972'
year: '2022'
...