---
_id: '54548'
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Prager RP, Trautmann H. Exploratory Landscape Analysis for Mixed-Variable Problems.
IEEE Transactions on Evolutionary Computation. Published online 2024:1-1.
doi:10.1109/TEVC.2024.3399560
apa: Prager, R. P., & Trautmann, H. (2024). Exploratory Landscape Analysis for
Mixed-Variable Problems. IEEE Transactions on Evolutionary Computation,
1–1. https://doi.org/10.1109/TEVC.2024.3399560
bibtex: '@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for
Mixed-Variable Problems}, DOI={10.1109/TEVC.2024.3399560},
journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael
Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }'
chicago: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, 1–1. https://doi.org/10.1109/TEVC.2024.3399560.
ieee: 'R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable
Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024,
doi: 10.1109/TEVC.2024.3399560.'
mla: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, pp. 1–1, doi:10.1109/TEVC.2024.3399560.
short: R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation
(2024) 1–1.
date_created: 2024-06-03T06:16:33Z
date_updated: 2024-06-03T06:17:13Z
department:
- _id: '819'
doi: 10.1109/TEVC.2024.3399560
keyword:
- Optimization
- Evolutionary computation
- Benchmark testing
- Hyperparameter optimization
- Portfolios
- Extraterrestrial measurements
- Dispersion
- Exploratory landscape analysis
- mixed-variable problem
- mixed search spaces
- automated algorithm selection
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Exploratory Landscape Analysis for Mixed-Variable Problems
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '56277'
abstract:
- lang: eng
text: What is learner-sensitive feedback to argumentative learner texts when it
is to be issued computer- based? Learning stages are difficult to quantify. The
paper provides insight into the history of research since the 1980s and a preview
of what this automated feedback might look like. These questions are embedded
in a research project at the Universities of Paderborn and Hannover, Germany,
from which a software (project name ArgSchool) emerges that will provide such
feedback.
author:
- first_name: Sebastian
full_name: Kilsbach, Sebastian
id: '93839'
last_name: Kilsbach
- first_name: Nadine
full_name: Michel, Nadine
id: '47857'
last_name: Michel
citation:
ama: 'Kilsbach S, Michel N. Computer-Based Generation of Learner-Sensitive Feedback
to Argumentative Learner Texts. In: Proceedings of the Tenth Conference of
the International Society for the Study of Argumentation. ; 2024.'
apa: Kilsbach, S., & Michel, N. (2024). Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts. Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation. Tenth Conference
of the International Society for the Study of Argumentation, Leiden.
bibtex: '@inproceedings{Kilsbach_Michel_2024, title={Computer-Based Generation of
Learner-Sensitive Feedback to Argumentative Learner Texts}, booktitle={Proceedings
of the Tenth Conference of the International Society for the Study of Argumentation},
author={Kilsbach, Sebastian and Michel, Nadine}, year={2024} }'
chicago: Kilsbach, Sebastian, and Nadine Michel. “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts.” In Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation, 2024.
ieee: S. Kilsbach and N. Michel, “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts,” presented at the Tenth Conference of
the International Society for the Study of Argumentation, Leiden, 2024.
mla: Kilsbach, Sebastian, and Nadine Michel. “Computer-Based Generation of Learner-Sensitive
Feedback to Argumentative Learner Texts.” Proceedings of the Tenth Conference
of the International Society for the Study of Argumentation, 2024.
short: 'S. Kilsbach, N. Michel, in: Proceedings of the Tenth Conference of the International
Society for the Study of Argumentation, 2024.'
conference:
end_date: 2023-07-07
location: Leiden
name: Tenth Conference of the International Society for the Study of Argumentation
start_date: 2023-07-04
date_created: 2024-09-30T09:24:12Z
date_updated: 2024-09-30T09:25:14Z
keyword:
- AI
- argumentation mining
- discourse history
- (automated
- learner-sensitive) feedback
language:
- iso: eng
publication: Proceedings of the Tenth Conference of the International Society for
the Study of Argumentation
status: public
title: Computer-Based Generation of Learner-Sensitive Feedback to Argumentative Learner
Texts
type: conference
user_id: '47857'
year: '2024'
...
---
_id: '52662'
abstract:
- lang: eng
text: Static analysis tools support developers in detecting potential coding issues,
such as bugs or vulnerabilities. Research emphasizes technical challenges of such
tools but also mentions severe usability shortcomings. These shortcomings hinder
the adoption of static analysis tools, and user dissatisfaction may even lead
to tool abandonment. To comprehensively assess the state of the art, we present
the first systematic usability evaluation of a wide range of static analysis tools.
We derived a set of 36 relevant criteria from the literature and used them to
evaluate a total of 46 static analysis tools complying with our inclusion and
exclusion criteria - a representative set of mainly non-proprietary tools. The
evaluation against the usability criteria in a multiple-raters approach shows
that two thirds of the considered tools off er poor warning messages, while about
three-quarters provide hardly any fix support. Furthermore, the integration of
user knowledge is strongly neglected, which could be used for instance, to improve
handling of false positives. Finally, issues regarding workflow integration and
specialized user interfaces are revealed. These findings should prove useful in
guiding and focusing further research and development in user experience for static
code analyses.
author:
- first_name: Marcus
full_name: Nachtigall, Marcus
id: '41213'
last_name: Nachtigall
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Nachtigall M, Schlichtig M, Bodden E. Evaluation of Usability Criteria Addressed
by Static Analysis Tools on a Large Scale. In: Software Engineering 2023.
Gesellschaft für Informatik e.V.; 2023:95–96.'
apa: Nachtigall, M., Schlichtig, M., & Bodden, E. (2023). Evaluation of Usability
Criteria Addressed by Static Analysis Tools on a Large Scale. In Software Engineering
2023 (pp. 95–96). Gesellschaft für Informatik e.V.
bibtex: '@inbook{Nachtigall_Schlichtig_Bodden_2023, place={Bonn}, title={Evaluation
of Usability Criteria Addressed by Static Analysis Tools on a Large Scale}, booktitle={Software
Engineering 2023}, publisher={Gesellschaft für Informatik e.V.}, author={Nachtigall,
Marcus and Schlichtig, Michael and Bodden, Eric}, year={2023}, pages={95–96} }'
chicago: 'Nachtigall, Marcus, Michael Schlichtig, and Eric Bodden. “Evaluation of
Usability Criteria Addressed by Static Analysis Tools on a Large Scale.” In Software
Engineering 2023, 95–96. Bonn: Gesellschaft für Informatik e.V., 2023.'
ieee: 'M. Nachtigall, M. Schlichtig, and E. Bodden, “Evaluation of Usability Criteria
Addressed by Static Analysis Tools on a Large Scale,” in Software Engineering
2023, Bonn: Gesellschaft für Informatik e.V., 2023, pp. 95–96.'
mla: Nachtigall, Marcus, et al. “Evaluation of Usability Criteria Addressed by Static
Analysis Tools on a Large Scale.” Software Engineering 2023, Gesellschaft
für Informatik e.V., 2023, pp. 95–96.
short: 'M. Nachtigall, M. Schlichtig, E. Bodden, in: Software Engineering 2023,
Gesellschaft für Informatik e.V., Bonn, 2023, pp. 95–96.'
date_created: 2024-03-20T09:26:29Z
date_updated: 2024-03-20T09:27:41Z
department:
- _id: '76'
keyword:
- Automated static analysis
- Software usability
language:
- iso: eng
main_file_link:
- url: https://dl.gi.de/items/5afe477f-2f6a-4b3d-b391-f024baf0b7a5
page: 95–96
place: Bonn
publication: Software Engineering 2023
publication_identifier:
isbn:
- 978-3-88579-726-5
publisher: Gesellschaft für Informatik e.V.
status: public
title: Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large
Scale
type: book_chapter
user_id: '32312'
year: '2023'
...
---
_id: '52816'
abstract:
- lang: eng
text: "Manufacturing companies face the challenge of reaching required quality standards.
Using\r\noptical sensors and deep learning might help. However, training deep
learning algorithms\r\nrequire large amounts of visual training data. Using domain
randomization to generate synthetic\r\nimage data can alleviate this bottleneck.
This paper presents the application of synthetic\r\nimage training data for optical
quality inspections using visual sensor technology. The results\r\nshow synthetically
generated training data are appropriate for visual quality inspections."
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Hieb, Michael
id: '72252'
last_name: Hieb
citation:
ama: 'Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in
Machine Vision Quality Gates in Manufacturing. In: Lectures. AMA Service
GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:10.5162/smsi2023/d7.4'
apa: Gräßler, I., & Hieb, M. (2023). Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing. Lectures,
253–524. https://doi.org/10.5162/smsi2023/d7.4
bibtex: '@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={10.5162/smsi2023/d7.4},
booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515
Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524}
}'
chicago: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing.” In Lectures,
253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023.
https://doi.org/10.5162/smsi2023/d7.4.
ieee: 'I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection
in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg,
2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.'
mla: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing.” Lectures,
AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp.
253–524, doi:10.5162/smsi2023/d7.4.
short: 'I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str.
49, 31515 Wunstorf, Germany, 2023, pp. 253–524.'
conference:
end_date: 2023-05-11
location: Nuremberg
name: SMSI 2023. Sensor and Measurement Science International
start_date: 2023-05-08
date_created: 2024-03-25T10:16:24Z
date_updated: 2024-03-25T11:05:53Z
department:
- _id: '152'
doi: 10.5162/smsi2023/d7.4
keyword:
- synthetic training data
- machine vision quality gates
- deep learning
- automated inspection and quality control
- production control
language:
- iso: eng
page: 253-524
publication: Lectures
publication_status: published
publisher: AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany
quality_controlled: '1'
status: public
title: Creating Synthetic Training Datasets for Inspection in Machine Vision Quality
Gates in Manufacturing
type: conference
user_id: '5905'
year: '2023'
...
---
_id: '32410'
abstract:
- lang: eng
text: "Static analysis tools support developers in detecting potential coding issues,
such as bugs or vulnerabilities. Research on static analysis emphasizes its technical
challenges but also mentions severe usability shortcomings. These shortcomings
hinder the adoption of static analysis tools, and in some cases, user dissatisfaction
even leads to tool abandonment.\r\nTo comprehensively assess the current state
of the art, this paper presents the first systematic usability evaluation in a
wide range of static analysis tools. We derived a set of 36 relevant criteria
from the scientific literature and gathered a collection of 46 static analysis
tools complying with our inclusion and exclusion criteria - a representative set
of mainly non-proprietary tools. Then, we evaluated how well these tools fulfill
the aforementioned criteria.\r\nThe evaluation shows that more than half of the
considered tools offer poor warning messages, while about three-quarters of the
tools provide hardly any fix support. Furthermore, the integration of user knowledge
is strongly neglected, which could be used for improved handling of false positives
and tuning the results for the corresponding developer. Finally, issues regarding
workflow integration and specialized user interfaces are proved further.\r\nThese
findings should prove useful in guiding and focusing further research and development
in the area of user experience for static code analyses."
author:
- first_name: Marcus
full_name: Nachtigall, Marcus
id: '41213'
last_name: Nachtigall
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Nachtigall M, Schlichtig M, Bodden E. A Large-Scale Study of Usability Criteria
Addressed by Static Analysis Tools. In: Proceedings of the 31st ACM SIGSOFT
International Symposium on Software Testing and Analysis. ACM; 2022:532-543.
doi:10.1145/3533767'
apa: Nachtigall, M., Schlichtig, M., & Bodden, E. (2022). A Large-Scale Study
of Usability Criteria Addressed by Static Analysis Tools. Proceedings of the
31st ACM SIGSOFT International Symposium on Software Testing and Analysis,
532–543. https://doi.org/10.1145/3533767
bibtex: '@inproceedings{Nachtigall_Schlichtig_Bodden_2022, title={A Large-Scale
Study of Usability Criteria Addressed by Static Analysis Tools}, DOI={10.1145/3533767},
booktitle={Proceedings of the 31st ACM SIGSOFT International Symposium on Software
Testing and Analysis}, publisher={ACM}, author={Nachtigall, Marcus and Schlichtig,
Michael and Bodden, Eric}, year={2022}, pages={532–543} }'
chicago: Nachtigall, Marcus, Michael Schlichtig, and Eric Bodden. “A Large-Scale
Study of Usability Criteria Addressed by Static Analysis Tools.” In Proceedings
of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis,
532–43. ACM, 2022. https://doi.org/10.1145/3533767.
ieee: 'M. Nachtigall, M. Schlichtig, and E. Bodden, “A Large-Scale Study of Usability
Criteria Addressed by Static Analysis Tools,” in Proceedings of the 31st ACM
SIGSOFT International Symposium on Software Testing and Analysis, 2022, pp.
532–543, doi: 10.1145/3533767.'
mla: Nachtigall, Marcus, et al. “A Large-Scale Study of Usability Criteria Addressed
by Static Analysis Tools.” Proceedings of the 31st ACM SIGSOFT International
Symposium on Software Testing and Analysis, ACM, 2022, pp. 532–43, doi:10.1145/3533767.
short: 'M. Nachtigall, M. Schlichtig, E. Bodden, in: Proceedings of the 31st ACM
SIGSOFT International Symposium on Software Testing and Analysis, ACM, 2022, pp.
532–543.'
date_created: 2022-07-25T08:02:36Z
date_updated: 2022-07-26T11:42:23Z
department:
- _id: '76'
doi: 10.1145/3533767
keyword:
- Automated static analysis
- Software usability
language:
- iso: eng
page: 532 - 543
publication: Proceedings of the 31st ACM SIGSOFT International Symposium on Software
Testing and Analysis
publication_identifier:
isbn:
- '9781450393799'
publication_status: published
publisher: ACM
quality_controlled: '1'
related_material:
link:
- relation: confirmation
url: https://dl.acm.org/doi/10.1145/3533767.3534374
status: public
title: A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools
type: conference
user_id: '32312'
year: '2022'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '48881'
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) a k-nearest neighbor graph
(NNG) transformation of the input instance. To this end we theoretically derive
minimum and maximum values for properties of MSTs and k-NNGs 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. Eventually, 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
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential
of Normalized TSP Features for Automated Algorithm Selection. In: Proceedings
of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association
for Computing Machinery; 2021:1–15.'
apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke,
P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm
Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
bibtex: '@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York,
NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm
Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins,
Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann,
Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }'
chicago: 'Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann,
and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 1–15. New York, NY, USA: Association for Computing
Machinery, 2021.'
ieee: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On
the Potential of Normalized TSP Features for Automated Algorithm Selection,” in
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.'
mla: Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, Association for Computing Machinery, 2021, pp. 1–15.
short: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in:
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:23Z
department:
- _id: '819'
extern: '1'
keyword:
- automated algorithm selection
- graph theory
- instance features
- normalization
- traveling salesperson problem (TSP)
language:
- iso: eng
page: 1–15
place: New York, NY, USA
publication: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-8352-3
publisher: Association for Computing Machinery
status: public
title: On the Potential of Normalized TSP Features for Automated Algorithm Selection
type: book_chapter
user_id: '102979'
year: '2021'
...
---
_id: '48897'
abstract:
- lang: eng
text: 'In this work we focus on the well-known Euclidean Traveling Salesperson Problem
(TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in
the context of per-instance algorithm selection (AS). We evolve instances with
nodes where the solvers show strongly different performance profiles. These instances
serve as a basis for an exploratory study on the identification of well-discriminating
problem characteristics (features). Our results in a nutshell: we show that even
though (1) promising features exist, (2) these are in line with previous results
from the literature, and (3) models trained with these features are more accurate
than models adopting sophisticated feature selection methods, the advantage is
not close to the virtual best solver in terms of penalized average runtime and
so is the performance gain over the single best solver. However, we show that
a feature-free deep neural network based approach solely based on visual representation
of the instances already matches classical AS model results and thus shows huge
potential for future studies.'
author:
- first_name: Moritz
full_name: Seiler, Moritz
last_name: Seiler
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive
Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson
Problem. In: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag;
2020:48–64. doi:10.1007/978-3-030-58112-1_4'
apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection
on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature}
(PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
bibtex: '@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin,
Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem}, DOI={10.1007/978-3-030-58112-1_4},
booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag},
author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal
and Trautmann, Heike}, year={2020}, pages={48–64} }'
chicago: 'Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike
Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem.” In Parallel Problem
Solving from {Nature} (PPSN XVI), 48–64. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_4.'
ieee: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning
as a Competitive Feature-Free Approach for Automated Algorithm Selection on the
Traveling Salesperson Problem,” in Parallel Problem Solving from {Nature} (PPSN
XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.'
mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach
for Automated Algorithm Selection on the Traveling Salesperson Problem.” Parallel
Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, 2020, pp. 48–64,
doi:10.1007/978-3-030-58112-1_4.
short: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem
Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
48–64.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:45Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_4
extern: '1'
keyword:
- Automated algorithm selection
- Deep learning
- Feature-based approaches
- Traveling Salesperson Problem
language:
- iso: eng
page: 48–64
place: Berlin, Heidelberg
publication: Parallel Problem Solving from {Nature} (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publisher: Springer-Verlag
status: public
title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm
Selection on the Traveling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '3852'
abstract:
- lang: eng
text: "In automated machine learning (AutoML), the process of engineering machine
learning applications with respect to a specific problem is (partially) automated.\r\nVarious
AutoML tools have already been introduced to provide out-of-the-box machine learning
functionality.\r\nMore specifically, by selecting machine learning algorithms
and optimizing their hyperparameters, these tools produce a machine learning pipeline
tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
follows an evolutionary approach, it suffers from performance issues when dealing
with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
a hierarchical planning to configure machine learning pipelines that are unlimited
in length.\r\nWe evaluate our approach and find its performance to be competitive
with other AutoML tools, including TPOT."
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...
---
_id: '48884'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
on a large set of well-known benchmark instances and demonstrate complementary
performance, in that different instances may be solved most effectively by different
algorithms. We leverage this complementarity to build an algorithm selector, which
selects the best TSP solver on a per-instance basis and thus achieves significantly
improved performance compared to the single best solver, representing an advance
in the state of the art in solving the Euclidean TSP. Our in-depth analysis of
the selectors provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620.
doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff,
Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018},
pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation,
vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...