---
_id: '63708'
author:
- first_name: Simon
full_name: Klüttermann, Simon
last_name: Klüttermann
- first_name: Jérôme
full_name: Rutinowski, Jérôme
last_name: Rutinowski
- first_name: Frederik
full_name: Polachowski, Frederik
last_name: Polachowski
- first_name: Anh
full_name: Nguyen, Anh
last_name: Nguyen
- first_name: Britta
full_name: Grimme, Britta
last_name: Grimme
- first_name: Moritz
full_name: Roidl, Moritz
last_name: Roidl
- first_name: Emmanuel
full_name: Müller, Emmanuel
last_name: Müller
citation:
ama: 'Klüttermann S, Rutinowski J, Polachowski F, et al. On the Effectiveness of
Heterogeneous Ensemble Methods for Re-Identification. In: Wani MA, Angelov P,
Luo F, et al., eds. International Conference on Machine Learning and Applications,
ICMLA 2024, Miami, FL, USA, December 18-20, 2024. IEEE; 2024:1705–1711. doi:10.1109/ICMLA61862.2024.00263'
apa: Klüttermann, S., Rutinowski, J., Polachowski, F., Nguyen, A., Grimme, B., Roidl,
M., & Müller, E. (2024). On the Effectiveness of Heterogeneous Ensemble Methods
for Re-Identification. In M. A. Wani, P. Angelov, F. Luo, M. Ogihara, X. Wu, R.-E.
Precup, R. Ramezani, & X. Gu (Eds.), International Conference on Machine
Learning and Applications, ICMLA 2024, Miami, FL, USA, December 18-20, 2024
(pp. 1705–1711). IEEE. https://doi.org/10.1109/ICMLA61862.2024.00263
bibtex: '@inproceedings{Klüttermann_Rutinowski_Polachowski_Nguyen_Grimme_Roidl_Müller_2024,
title={On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification},
DOI={10.1109/ICMLA61862.2024.00263},
booktitle={International Conference on Machine Learning and Applications, ICMLA
2024, Miami, FL, USA, December 18-20, 2024}, publisher={IEEE}, author={Klüttermann,
Simon and Rutinowski, Jérôme and Polachowski, Frederik and Nguyen, Anh and Grimme,
Britta and Roidl, Moritz and Müller, Emmanuel}, editor={Wani, M. Arif and Angelov,
Plamen and Luo, Feng and Ogihara, Mitsunori and Wu, Xintao and Precup, Radu-Emil
and Ramezani, Ramin and Gu, Xiaowei}, year={2024}, pages={1705–1711} }'
chicago: Klüttermann, Simon, Jérôme Rutinowski, Frederik Polachowski, Anh Nguyen,
Britta Grimme, Moritz Roidl, and Emmanuel Müller. “On the Effectiveness of Heterogeneous
Ensemble Methods for Re-Identification.” In International Conference on Machine
Learning and Applications, ICMLA 2024, Miami, FL, USA, December 18-20, 2024,
edited by M. Arif Wani, Plamen Angelov, Feng Luo, Mitsunori Ogihara, Xintao Wu,
Radu-Emil Precup, Ramin Ramezani, and Xiaowei Gu, 1705–1711. IEEE, 2024. https://doi.org/10.1109/ICMLA61862.2024.00263.
ieee: 'S. Klüttermann et al., “On the Effectiveness of Heterogeneous Ensemble
Methods for Re-Identification,” in International Conference on Machine Learning
and Applications, ICMLA 2024, Miami, FL, USA, December 18-20, 2024, 2024,
pp. 1705–1711, doi: 10.1109/ICMLA61862.2024.00263.'
mla: Klüttermann, Simon, et al. “On the Effectiveness of Heterogeneous Ensemble
Methods for Re-Identification.” International Conference on Machine Learning
and Applications, ICMLA 2024, Miami, FL, USA, December 18-20, 2024, edited
by M. Arif Wani et al., IEEE, 2024, pp. 1705–1711, doi:10.1109/ICMLA61862.2024.00263.
short: 'S. Klüttermann, J. Rutinowski, F. Polachowski, A. Nguyen, B. Grimme, M.
Roidl, E. Müller, in: M.A. Wani, P. Angelov, F. Luo, M. Ogihara, X. Wu, R.-E.
Precup, R. Ramezani, X. Gu (Eds.), International Conference on Machine Learning
and Applications, ICMLA 2024, Miami, FL, USA, December 18-20, 2024, IEEE, 2024,
pp. 1705–1711.'
date_created: 2026-01-22T14:50:26Z
date_updated: 2026-01-22T14:53:20Z
department:
- _id: '819'
doi: 10.1109/ICMLA61862.2024.00263
editor:
- first_name: M. Arif
full_name: Wani, M. Arif
last_name: Wani
- first_name: Plamen
full_name: Angelov, Plamen
last_name: Angelov
- first_name: Feng
full_name: Luo, Feng
last_name: Luo
- first_name: Mitsunori
full_name: Ogihara, Mitsunori
last_name: Ogihara
- first_name: Xintao
full_name: Wu, Xintao
last_name: Wu
- first_name: Radu-Emil
full_name: Precup, Radu-Emil
last_name: Precup
- first_name: Ramin
full_name: Ramezani, Ramin
last_name: Ramezani
- first_name: Xiaowei
full_name: Gu, Xiaowei
last_name: Gu
page: 1705–1711
publication: International Conference on Machine Learning and Applications, ICMLA
2024, Miami, FL, USA, December 18-20, 2024
publisher: IEEE
status: public
title: On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification
type: conference
user_id: '15504'
year: '2024'
...
---
_id: '63705'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Bossek J, Grimme C. Generalised Kruskal Mutation for the Multi-Objective Minimum
Spanning Tree Problem. In: Li X, Handl J, eds. Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July
14-18, 2024. ACM; 2024. doi:10.1145/3638529.3654165'
apa: Bossek, J., & Grimme, C. (2024). Generalised Kruskal Mutation for the Multi-Objective
Minimum Spanning Tree Problem. In X. Li & J. Handl (Eds.), Proceedings
of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne,
VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654165
bibtex: '@inproceedings{Bossek_Grimme_2024, title={Generalised Kruskal Mutation
for the Multi-Objective Minimum Spanning Tree Problem}, DOI={10.1145/3638529.3654165},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024}, publisher={ACM}, author={Bossek,
Jakob and Grimme, Christian}, editor={Li, Xiaodong and Handl, Julia}, year={2024}
}'
chicago: Bossek, Jakob, and Christian Grimme. “Generalised Kruskal Mutation for
the Multi-Objective Minimum Spanning Tree Problem.” In Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia,
July 14-18, 2024, edited by Xiaodong Li and Julia Handl. ACM, 2024. https://doi.org/10.1145/3638529.3654165.
ieee: 'J. Bossek and C. Grimme, “Generalised Kruskal Mutation for the Multi-Objective
Minimum Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024,
2024, doi: 10.1145/3638529.3654165.'
mla: Bossek, Jakob, and Christian Grimme. “Generalised Kruskal Mutation for the
Multi-Objective Minimum Spanning Tree Problem.” Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia,
July 14-18, 2024, edited by Xiaodong Li and Julia Handl, ACM, 2024, doi:10.1145/3638529.3654165.
short: 'J. Bossek, C. Grimme, in: X. Li, J. Handl (Eds.), Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia,
July 14-18, 2024, ACM, 2024.'
date_created: 2026-01-22T14:43:22Z
date_updated: 2026-01-22T14:46:01Z
department:
- _id: '819'
doi: 10.1145/3638529.3654165
editor:
- first_name: Xiaodong
full_name: Li, Xiaodong
last_name: Li
- first_name: Julia
full_name: Handl, Julia
last_name: Handl
language:
- iso: eng
publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2024, Melbourne, VIC, Australia, July 14-18, 2024
publisher: ACM
status: public
title: Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree
Problem
type: conference
user_id: '15504'
year: '2024'
...
---
_id: '63658'
author:
- first_name: Simon
full_name: Klüttermann, Simon
last_name: Klüttermann
- first_name: Jérôme
full_name: Rutinowski, Jérôme
last_name: Rutinowski
- first_name: Frederik
full_name: Polachowski, Frederik
last_name: Polachowski
- first_name: Anh
full_name: Nguyen, Anh
last_name: Nguyen
- first_name: Britta
full_name: Grimme, Britta
last_name: Grimme
- first_name: Moritz
full_name: Roidl, Moritz
last_name: Roidl
- first_name: Emmanuel
full_name: Müller, Emmanuel
last_name: Müller
citation:
ama: 'Klüttermann S, Rutinowski J, Polachowski F, et al. On the Effectiveness of
Heterogeneous Ensemble Methods for Re-identification. In: 2024 International
Conference on Machine Learning and Applications (ICMLA). ; 2024:1705–1711.'
apa: Klüttermann, S., Rutinowski, J., Polachowski, F., Nguyen, A., Grimme, B., Roidl,
M., & Müller, E. (2024). On the Effectiveness of Heterogeneous Ensemble Methods
for Re-identification. 2024 International Conference on Machine Learning and
Applications (ICMLA), 1705–1711.
bibtex: '@inproceedings{Klüttermann_Rutinowski_Polachowski_Nguyen_Grimme_Roidl_Müller_2024,
title={On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification},
booktitle={2024 International Conference on Machine Learning and Applications
(ICMLA)}, author={Klüttermann, Simon and Rutinowski, Jérôme and Polachowski, Frederik
and Nguyen, Anh and Grimme, Britta and Roidl, Moritz and Müller, Emmanuel}, year={2024},
pages={1705–1711} }'
chicago: Klüttermann, Simon, Jérôme Rutinowski, Frederik Polachowski, Anh Nguyen,
Britta Grimme, Moritz Roidl, and Emmanuel Müller. “On the Effectiveness of Heterogeneous
Ensemble Methods for Re-Identification.” In 2024 International Conference on
Machine Learning and Applications (ICMLA), 1705–1711, 2024.
ieee: S. Klüttermann et al., “On the Effectiveness of Heterogeneous Ensemble
Methods for Re-identification,” in 2024 International Conference on Machine
Learning and Applications (ICMLA), 2024, pp. 1705–1711.
mla: Klüttermann, Simon, et al. “On the Effectiveness of Heterogeneous Ensemble
Methods for Re-Identification.” 2024 International Conference on Machine Learning
and Applications (ICMLA), 2024, pp. 1705–1711.
short: 'S. Klüttermann, J. Rutinowski, F. Polachowski, A. Nguyen, B. Grimme, M.
Roidl, E. Müller, in: 2024 International Conference on Machine Learning and Applications
(ICMLA), 2024, pp. 1705–1711.'
date_created: 2026-01-19T11:29:47Z
date_updated: 2026-03-19T07:47:29Z
department:
- _id: '819'
language:
- iso: eng
page: 1705–1711
publication: 2024 International Conference on Machine Learning and Applications (ICMLA)
status: public
title: On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
type: conference
user_id: '103682'
year: '2024'
...
---
_id: '47522'
abstract:
- lang: eng
text: Artificial benchmark functions are commonly used in optimization research
because of their ability to rapidly evaluate potential solutions, making them
a preferred substitute for real-world problems. However, these benchmark functions
have faced criticism for their limited resemblance to real-world problems. In
response, recent research has focused on automatically generating new benchmark
functions for areas where established test suites are inadequate. These approaches
have limitations, such as the difficulty of generating new benchmark functions
that exhibit exploratory landscape analysis (ELA) features beyond those of existing
benchmarks.The objective of this work is to develop a method for generating benchmark
functions for single-objective continuous optimization with user-specified structural
properties. Specifically, we aim to demonstrate a proof of concept for a method
that uses an ELA feature vector to specify these properties in advance. To achieve
this, we begin by generating a random sample of decision space variables and objective
values. We then adjust the objective values using CMA-ES until the corresponding
features of our new problem match the predefined ELA features within a specified
threshold. By iteratively transforming the landscape in this way, we ensure that
the resulting function exhibits the desired properties. To create the final function,
we use the resulting point cloud as training data for a simple neural network
that produces a function exhibiting the target ELA features. We demonstrate the
effectiveness of this approach by replicating the existing functions of the well-known
BBOB suite and creating new functions with ELA feature values that are not present
in BBOB.
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Konstantin
full_name: Dietrich, Konstantin
last_name: Dietrich
- first_name: Lennart
full_name: Schneider, Lennart
last_name: Schneider
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- 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: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
citation:
ama: 'Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark
Functions Optimized for Exploratory Landscape Features. In: Proceedings of
the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA
’23. Association for Computing Machinery; 2023:129–139. doi:10.1145/3594805.3607136'
apa: Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke,
P., Trautmann, H., & Mersmann, O. (2023). Neural Networks as Black-Box Benchmark
Functions Optimized for Exploratory Landscape Features. Proceedings of the
17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139.
https://doi.org/10.1145/3594805.3607136
bibtex: '@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023,
place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box
Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={10.1145/3594805.3607136},
booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms}, publisher={Association for Computing Machinery}, author={Prager,
Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier,
Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann,
Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }'
chicago: 'Prager, Raphael Patrick, Konstantin Dietrich, Lennart Schneider, Lennart
Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, and Olaf Mersmann.
“Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape
Features.” In Proceedings of the 17th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 129–139. FOGA ’23. New York, NY, USA: Association for
Computing Machinery, 2023. https://doi.org/10.1145/3594805.3607136.'
ieee: 'R. P. Prager et al., “Neural Networks as Black-Box Benchmark Functions
Optimized for Exploratory Landscape Features,” in Proceedings of the 17th ACM/SIGEVO
Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139, doi: 10.1145/3594805.3607136.'
mla: Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions
Optimized for Exploratory Landscape Features.” Proceedings of the 17th ACM/SIGEVO
Conference on Foundations of Genetic Algorithms, Association for Computing
Machinery, 2023, pp. 129–139, doi:10.1145/3594805.3607136.
short: 'R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke,
H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on
Foundations of Genetic Algorithms, Association for Computing Machinery, New York,
NY, USA, 2023, pp. 129–139.'
date_created: 2023-09-27T15:43:17Z
date_updated: 2023-10-16T12:33:02Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3594805.3607136
keyword:
- Benchmarking
- Instance Generator
- Black-Box Continuous Optimization
- Exploratory Landscape Analysis
- Neural Networks
language:
- iso: eng
page: 129–139
place: New York, NY, USA
publication: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- '9798400702020'
publisher: Association for Computing Machinery
series_title: FOGA ’23
status: public
title: Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory
Landscape Features
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '46297'
abstract:
- lang: eng
text: Exploratory landscape analysis (ELA) in single-objective black-box optimization
relies on a comprehensive and large set of numerical features characterizing problem
instances. Those foster problem understanding and serve as basis for constructing
automated algorithm selection models choosing the best suited algorithm for a
problem at hand based on the aforementioned features computed prior to optimization.
This work specifically points to the sensitivity of a substantial proportion of
these features to absolute objective values, i.e., we observe a lack of shift
and scale invariance. We show that this unfortunately induces bias within automated
algorithm selection models, an overfitting to specific benchmark problem sets
used for training and thereby hinders generalization capabilities to unseen problems.
We tackle these issues by presenting an appropriate objective normalization to
be used prior to ELA feature computation and empirically illustrate the respective
effectiveness focusing on the BBOB benchmark set.
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. Nullifying the Inherent Bias of Non-invariant Exploratory
Landscape Analysis Features. In: Correia J, Smith S, Qaddoura R, eds. Applications
of Evolutionary Computation. Springer Nature Switzerland; 2023:411–425.'
apa: Prager, R. P., & Trautmann, H. (2023). Nullifying the Inherent Bias of
Non-invariant Exploratory Landscape Analysis Features. In J. Correia, S. Smith,
& R. Qaddoura (Eds.), Applications of Evolutionary Computation (pp.
411–425). Springer Nature Switzerland.
bibtex: '@inproceedings{Prager_Trautmann_2023, place={Cham}, title={Nullifying the
Inherent Bias of Non-invariant Exploratory Landscape Analysis Features}, booktitle={Applications
of Evolutionary Computation}, publisher={Springer Nature Switzerland}, author={Prager,
Raphael Patrick and Trautmann, Heike}, editor={Correia, João and Smith, Stephen
and Qaddoura, Raneem}, year={2023}, pages={411–425} }'
chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent
Bias of Non-Invariant Exploratory Landscape Analysis Features.” In Applications
of Evolutionary Computation, edited by João Correia, Stephen Smith, and Raneem
Qaddoura, 411–425. Cham: Springer Nature Switzerland, 2023.'
ieee: R. P. Prager and H. Trautmann, “Nullifying the Inherent Bias of Non-invariant
Exploratory Landscape Analysis Features,” in Applications of Evolutionary Computation,
2023, pp. 411–425.
mla: Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent Bias
of Non-Invariant Exploratory Landscape Analysis Features.” Applications of
Evolutionary Computation, edited by João Correia et al., Springer Nature Switzerland,
2023, pp. 411–425.
short: 'R.P. Prager, H. Trautmann, in: J. Correia, S. Smith, R. Qaddoura (Eds.),
Applications of Evolutionary Computation, Springer Nature Switzerland, Cham, 2023,
pp. 411–425.'
date_created: 2023-08-04T06:54:22Z
date_updated: 2023-10-16T12:36:45Z
department:
- _id: '819'
- _id: '34'
editor:
- first_name: João
full_name: Correia, João
last_name: Correia
- first_name: Stephen
full_name: Smith, Stephen
last_name: Smith
- first_name: Raneem
full_name: Qaddoura, Raneem
last_name: Qaddoura
language:
- iso: eng
page: 411–425
place: Cham
publication: Applications of Evolutionary Computation
publication_identifier:
isbn:
- 978-3-031-30229-9
publisher: Springer Nature Switzerland
status: public
title: Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis
Features
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '46298'
abstract:
- lang: eng
text: The design and choice of benchmark suites are ongoing topics of discussion
in the multi-objective optimization community. Some suites provide a good understanding
of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems.
However, they lack diversity in their landscape properties and do not provide
a mechanism for creating multiple distinct problem instances. Other suites, like
bi-objective BBOB, possess diverse and challenging landscape properties, but their
optima are not well understood and can only be approximated empirically without
any guarantees.
author:
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Schäpermeier L, Kerschke P, Grimme C, Trautmann H. Peak-A-Boo! Generating
Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In:
Emmerich M, Deutz A, Wang H, et al., eds. Evolutionary Multi-Criterion Optimization.
Springer Nature Switzerland; 2023:291–304.'
apa: Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023). Peak-A-Boo!
Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto
Sets. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K.
Miettinen, & I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization
(pp. 291–304). Springer Nature Switzerland.
bibtex: '@inproceedings{Schäpermeier_Kerschke_Grimme_Trautmann_2023, place={Cham},
title={Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems
with Precise Pareto Sets}, booktitle={Evolutionary Multi-Criterion Optimization},
publisher={Springer Nature Switzerland}, author={Schäpermeier, Lennart and Kerschke,
Pascal and Grimme, Christian and Trautmann, Heike}, editor={Emmerich, Michael
and Deutz, André and Wang, Hao and Kononova, Anna V. and Naujoks, Boris and Li,
Ke and Miettinen, Kaisa and Yevseyeva, Iryna}, year={2023}, pages={291–304} }'
chicago: 'Schäpermeier, Lennart, Pascal Kerschke, Christian Grimme, and Heike Trautmann.
“Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with
Precise Pareto Sets.” In Evolutionary Multi-Criterion Optimization, edited
by Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke
Li, Kaisa Miettinen, and Iryna Yevseyeva, 291–304. Cham: Springer Nature Switzerland,
2023.'
ieee: L. Schäpermeier, P. Kerschke, C. Grimme, and H. Trautmann, “Peak-A-Boo! Generating
Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets,” in
Evolutionary Multi-Criterion Optimization, 2023, pp. 291–304.
mla: Schäpermeier, Lennart, et al. “Peak-A-Boo! Generating Multi-Objective Multiple
Peaks Benchmark Problems with Precise Pareto Sets.” Evolutionary Multi-Criterion
Optimization, edited by Michael Emmerich et al., Springer Nature Switzerland,
2023, pp. 291–304.
short: 'L. Schäpermeier, P. Kerschke, C. Grimme, H. Trautmann, in: M. Emmerich,
A. Deutz, H. Wang, A.V. Kononova, B. Naujoks, K. Li, K. Miettinen, I. Yevseyeva
(Eds.), Evolutionary Multi-Criterion Optimization, Springer Nature Switzerland,
Cham, 2023, pp. 291–304.'
date_created: 2023-08-04T06:56:10Z
date_updated: 2023-10-16T12:36:17Z
department:
- _id: '819'
- _id: '34'
editor:
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
- first_name: André
full_name: Deutz, André
last_name: Deutz
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Boris
full_name: Naujoks, Boris
last_name: Naujoks
- first_name: Ke
full_name: Li, Ke
last_name: Li
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Iryna
full_name: Yevseyeva, Iryna
last_name: Yevseyeva
language:
- iso: eng
page: 291–304
place: Cham
publication: Evolutionary Multi-Criterion Optimization
publication_identifier:
isbn:
- 978-3-031-27250-9
publisher: Springer Nature Switzerland
status: public
title: Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with
Precise Pareto Sets
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '46299'
abstract:
- lang: eng
text: The herein proposed Python package pflacco provides a set of numerical features
to characterize single-objective continuous and constrained optimization problems.
Thereby, pflacco addresses two major challenges in the area optimization. Firstly,
it provides the means to develop an understanding of a given problem instance,
which is crucial for designing, selecting, or configuring optimization algorithms
in general. Secondly, these numerical features can be utilized in the research
streams of automated algorithm selection and configuration. While the majority
of these landscape features is already available in the R package flacco, our
Python implementation offers these tools to an even wider audience and thereby
promotes research interests and novel avenues in the area of optimization.
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. Pflacco: Feature-Based Landscape Analysis of Continuous
and Constrained Optimization Problems in Python. Evolutionary Computation.
Published online 2023:1–25. doi:10.1162/evco_a_00341'
apa: 'Prager, R. P., & Trautmann, H. (2023). Pflacco: Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary
Computation, 1–25. https://doi.org/10.1162/evco_a_00341'
bibtex: '@article{Prager_Trautmann_2023, title={Pflacco: Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems in Python}, DOI={10.1162/evco_a_00341}, journal={Evolutionary
Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2023},
pages={1–25} }'
chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based
Landscape Analysis of Continuous and Constrained Optimization Problems in Python.”
Evolutionary Computation, 2023, 1–25. https://doi.org/10.1162/evco_a_00341.'
ieee: 'R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis
of Continuous and Constrained Optimization Problems in Python,” Evolutionary
Computation, pp. 1–25, 2023, doi: 10.1162/evco_a_00341.'
mla: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems in Python.” Evolutionary
Computation, 2023, pp. 1–25, doi:10.1162/evco_a_00341.'
short: R.P. Prager, H. Trautmann, Evolutionary Computation (2023) 1–25.
date_created: 2023-08-04T07:01:33Z
date_updated: 2023-10-16T12:35:56Z
department:
- _id: '819'
- _id: '34'
doi: 10.1162/evco_a_00341
language:
- iso: eng
page: 1–25
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: 'Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization
Problems in Python'
type: journal_article
user_id: '15504'
year: '2023'
...
---
_id: '48869'
abstract:
- lang: eng
text: Evolutionary algorithms have been shown to obtain good solutions for complex
optimization problems in static and dynamic environments. It is important to understand
the behaviour of evolutionary algorithms for complex optimization problems that
also involve dynamic and/or stochastic components in a systematic way in order
to further increase their applicability to real-world problems. We investigate
the node weighted traveling salesperson problem (W-TSP), which provides an abstraction
of a wide range of weighted TSP problems, in dynamic settings. In the dynamic
setting of the problem, items that have to be collected as part of a TSP tour
change over time. We first present a dynamic setup for the dynamic W-TSP parameterized
by different types of changes that are applied to the set of items to be collected
when traversing the tour. Our first experimental investigations study the impact
of such changes on resulting optimized tours in order to provide structural insights
of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary
algorithms and study the impact of the mutation operators and the use of populations
with dealing with the dynamic changes to the node weights of the problem.
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. On the Impact of Basic Mutation Operators
and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling
Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation
Conference. GECCO’23. Association for Computing Machinery; 2023:248–256. doi:10.1145/3583131.3590384'
apa: Bossek, J., Neumann, A., & Neumann, F. (2023). On the Impact of Basic Mutation
Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted
Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary
Computation Conference, 248–256. https://doi.org/10.1145/3583131.3590384
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2023, place={New York, NY, USA},
series={GECCO’23}, title={On the Impact of Basic Mutation Operators and Populations
within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson
Problem}, DOI={10.1145/3583131.3590384},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Aneta and Neumann, Frank}, year={2023}, pages={248–256}, collection={GECCO’23}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “On the Impact of Basic
Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic
Weighted Traveling Salesperson Problem.” In Proceedings of the Genetic and
Evolutionary Computation Conference, 248–256. GECCO’23. New York, NY, USA:
Association for Computing Machinery, 2023. https://doi.org/10.1145/3583131.3590384.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “On the Impact of Basic Mutation Operators
and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling
Salesperson Problem,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2023, pp. 248–256, doi: 10.1145/3583131.3590384.'
mla: Bossek, Jakob, et al. “On the Impact of Basic Mutation Operators and Populations
within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson
Problem.” Proceedings of the Genetic and Evolutionary Computation Conference,
Association for Computing Machinery, 2023, pp. 248–256, doi:10.1145/3583131.3590384.
short: 'J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2023, pp. 248–256.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:27Z
department:
- _id: '819'
doi: 10.1145/3583131.3590384
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- re-optimization
- weighted traveling salesperson problem
language:
- iso: eng
page: 248–256
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- '9798400701191'
publisher: Association for Computing Machinery
series_title: GECCO’23
status: public
title: On the Impact of Basic Mutation Operators and Populations within Evolutionary
Algorithms for the Dynamic Weighted Traveling Salesperson Problem
type: conference
user_id: '102979'
year: '2023'
...
---
_id: '48872'
abstract:
- lang: eng
text: Quality diversity (QD) is a branch of evolutionary computation that gained
increasing interest in recent years. The Map-Elites QD approach defines a feature
space, i.e., a partition of the search space, and stores the best solution for
each cell of this space. We study a simple QD algorithm in the context of pseudo-Boolean
optimisation on the "number of ones" feature space, where the ith cell stores
the best solution amongst those with a number of ones in [(i - 1)k, ik - 1]. Here
k is a granularity parameter 1 {$\leq$} k {$\leq$} n+1. We give a tight bound
on the expected time until all cells are covered for arbitrary fitness functions
and for all k and analyse the expected optimisation time of QD on OneMax and other
problems whose structure aligns favourably with the feature space. On combinatorial
problems we show that QD finds a (1 - 1/e)-approximation when maximising any monotone
sub-modular function with a single uniform cardinality constraint efficiently.
Defining the feature space as the number of connected components of a connected
graph, we show that QD finds a minimum spanning tree in expected polynomial time.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Sudholt D. Runtime Analysis of Quality Diversity Algorithms. In:
Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’23.
Association for Computing Machinery; 2023:1546–1554. doi:10.1145/3583131.3590383'
apa: Bossek, J., & Sudholt, D. (2023). Runtime Analysis of Quality Diversity
Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference,
1546–1554. https://doi.org/10.1145/3583131.3590383
bibtex: '@inproceedings{Bossek_Sudholt_2023, place={New York, NY, USA}, series={GECCO’23},
title={Runtime Analysis of Quality Diversity Algorithms}, DOI={10.1145/3583131.3590383},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Sudholt,
Dirk}, year={2023}, pages={1546–1554}, collection={GECCO’23} }'
chicago: 'Bossek, Jakob, and Dirk Sudholt. “Runtime Analysis of Quality Diversity
Algorithms.” In Proceedings of the Genetic and Evolutionary Computation Conference,
1546–1554. GECCO’23. New York, NY, USA: Association for Computing Machinery, 2023.
https://doi.org/10.1145/3583131.3590383.'
ieee: 'J. Bossek and D. Sudholt, “Runtime Analysis of Quality Diversity Algorithms,”
in Proceedings of the Genetic and Evolutionary Computation Conference,
2023, pp. 1546–1554, doi: 10.1145/3583131.3590383.'
mla: Bossek, Jakob, and Dirk Sudholt. “Runtime Analysis of Quality Diversity Algorithms.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2023, pp. 1546–1554, doi:10.1145/3583131.3590383.
short: 'J. Bossek, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp.
1546–1554.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:48:26Z
department:
- _id: '819'
doi: 10.1145/3583131.3590383
extern: '1'
keyword:
- quality diversity
- runtime analysis
language:
- iso: eng
page: 1546–1554
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- '9798400701191'
publisher: Association for Computing Machinery
series_title: GECCO’23
status: public
title: Runtime Analysis of Quality Diversity Algorithms
type: conference
user_id: '102979'
year: '2023'
...
---
_id: '48886'
abstract:
- lang: eng
text: 'Generating new instances via evolutionary methods is commonly used to create
new benchmarking data-sets, with a focus on attempting to cover an instance-space
as completely as possible. Recent approaches have exploited Quality-Diversity
methods to evolve sets of instances that are both diverse and discriminatory with
respect to a portfolio of solvers, but these methods can be challenging when attempting
to find diversity in a high-dimensional feature-space. We address this issue by
training a model based on Principal Component Analysis on existing instances to
create a low-dimension projection of the high-dimension feature-vectors, and then
apply Novelty Search directly in the new low-dimension space. We conduct experiments
to evolve diverse and discriminatory instances of Knapsack Problems, comparing
the use of Novelty Search in the original feature-space to using Novelty Search
in a low-dimensional projection, and repeat over a given set of dimensions. We
find that the methods are complementary: if treated as an ensemble, they collectively
provide increased coverage of the space. Specifically, searching for novelty in
a low-dimension space contributes 56% of the filled regions of the space, while
searching directly in the feature-space covers the remaining 44%.'
author:
- first_name: Alejandro
full_name: Marrero, Alejandro
last_name: Marrero
- first_name: Eduardo
full_name: Segredo, Eduardo
last_name: Segredo
- first_name: Emma
full_name: Hart, Emma
last_name: Hart
- 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
citation:
ama: 'Marrero A, Segredo E, Hart E, Bossek J, Neumann A. Generating Diverse and
Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions
of Feature-Space. In: Proceedings of the Genetic} and Evolutionary Computation
Conference. GECCO’23. Association for Computing Machinery; 2023:312–320. doi:10.1145/3583131.3590504'
apa: Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating
Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable
Dimensions of Feature-Space. Proceedings of the Genetic} and Evolutionary Computation
Conference, 312–320. https://doi.org/10.1145/3583131.3590504
bibtex: '@inproceedings{Marrero_Segredo_Hart_Bossek_Neumann_2023, place={New York,
NY, USA}, series={GECCO’23}, title={Generating Diverse and Discriminatory Knapsack
Instances by Searching for Novelty in Variable Dimensions of Feature-Space}, DOI={10.1145/3583131.3590504}, booktitle={Proceedings
of the Genetic} and Evolutionary Computation Conference}, publisher={Association
for Computing Machinery}, author={Marrero, Alejandro and Segredo, Eduardo and
Hart, Emma and Bossek, Jakob and Neumann, Aneta}, year={2023}, pages={312–320},
collection={GECCO’23} }'
chicago: 'Marrero, Alejandro, Eduardo Segredo, Emma Hart, Jakob Bossek, and Aneta
Neumann. “Generating Diverse and Discriminatory Knapsack Instances by Searching
for Novelty in Variable Dimensions of Feature-Space.” In Proceedings of the
Genetic} and Evolutionary Computation Conference, 312–320. GECCO’23. New York,
NY, USA: Association for Computing Machinery, 2023. https://doi.org/10.1145/3583131.3590504.'
ieee: 'A. Marrero, E. Segredo, E. Hart, J. Bossek, and A. Neumann, “Generating Diverse
and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions
of Feature-Space,” in Proceedings of the Genetic} and Evolutionary Computation
Conference, 2023, pp. 312–320, doi: 10.1145/3583131.3590504.'
mla: Marrero, Alejandro, et al. “Generating Diverse and Discriminatory Knapsack
Instances by Searching for Novelty in Variable Dimensions of Feature-Space.” Proceedings
of the Genetic} and Evolutionary Computation Conference, Association for Computing
Machinery, 2023, pp. 312–320, doi:10.1145/3583131.3590504.
short: 'A. Marrero, E. Segredo, E. Hart, J. Bossek, A. Neumann, in: Proceedings
of the Genetic} and Evolutionary Computation Conference, Association for Computing
Machinery, New York, NY, USA, 2023, pp. 312–320.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:32Z
department:
- _id: '819'
doi: 10.1145/3583131.3590504
extern: '1'
keyword:
- evolutionary computation
- instance generation
- instance-space analysis
- knapsack problem
- novelty search
language:
- iso: eng
page: 312–320
place: New York, NY, USA
publication: Proceedings of the Genetic} and Evolutionary Computation Conference
publication_identifier:
isbn:
- '9798400701191'
publisher: Association for Computing Machinery
series_title: GECCO’23
status: public
title: Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty
in Variable Dimensions of Feature-Space
type: conference
user_id: '102979'
year: '2023'
...
---
_id: '48871'
abstract:
- lang: eng
text: 'Most runtime analyses of randomised search heuristics focus on the expected
number of function evaluations to find a unique global optimum. We ask a fundamental
question: if additional search points are declared optimal, or declared as desirable
target points, do these additional optima speed up evolutionary algorithms? More
formally, we analyse the expected hitting time of a target set OPT{$\cup$}S where
S is a set of non-optimal search points and OPT is the set of optima and compare
it to the expected hitting time of OPT. We show that the answer to our question
depends on the number and placement of search points in S. For all black-box algorithms
and all fitness functions with polynomial expected optimisation times we show
that, if additional optima are placed randomly, even an exponential number of
optima has a negligible effect on the expected optimisation time. Considering
Hamming balls around all global optima gives an easier target for some algorithms
and functions and can shift the phase transition with respect to offspring population
sizes in the (1,{$\lambda$}) EA on OneMax. However, for the one-dimensional Ising
model the time to reach Hamming balls of radius (1/2-{$ϵ$})n around optima does
not reduce the asymptotic expected optimisation time in the worst case. Finally,
on functions where search trajectories typically join in a single search point,
turning one search point into an optimum drastically reduces the expected optimisation
time.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: Bossek J, Sudholt D. Do Additional Target Points Speed Up Evolutionary Algorithms?
Theoretical Computer Science. Published online 2023:113757. doi:10.1016/j.tcs.2023.113757
apa: Bossek, J., & Sudholt, D. (2023). Do Additional Target Points Speed Up
Evolutionary Algorithms? Theoretical Computer Science, 113757. https://doi.org/10.1016/j.tcs.2023.113757
bibtex: '@article{Bossek_Sudholt_2023, title={Do Additional Target Points Speed
Up Evolutionary Algorithms?}, DOI={10.1016/j.tcs.2023.113757},
journal={Theoretical Computer Science}, author={Bossek, Jakob and Sudholt, Dirk},
year={2023}, pages={113757} }'
chicago: Bossek, Jakob, and Dirk Sudholt. “Do Additional Target Points Speed Up
Evolutionary Algorithms?” Theoretical Computer Science, 2023, 113757. https://doi.org/10.1016/j.tcs.2023.113757.
ieee: 'J. Bossek and D. Sudholt, “Do Additional Target Points Speed Up Evolutionary
Algorithms?,” Theoretical Computer Science, p. 113757, 2023, doi: 10.1016/j.tcs.2023.113757.'
mla: Bossek, Jakob, and Dirk Sudholt. “Do Additional Target Points Speed Up Evolutionary
Algorithms?” Theoretical Computer Science, 2023, p. 113757, doi:10.1016/j.tcs.2023.113757.
short: J. Bossek, D. Sudholt, Theoretical Computer Science (2023) 113757.
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:51:07Z
department:
- _id: '819'
doi: 10.1016/j.tcs.2023.113757
keyword:
- Evolutionary algorithms
- pseudo-Boolean functions
- runtime analysis
language:
- iso: eng
page: '113757'
publication: Theoretical Computer Science
publication_identifier:
issn:
- 0304-3975
status: public
title: Do Additional Target Points Speed Up Evolutionary Algorithms?
type: journal_article
user_id: '102979'
year: '2023'
...
---
_id: '48859'
abstract:
- lang: eng
text: We contribute to the efficient approximation of the Pareto-set for the classical
NP-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary
computation. More precisely, by building upon preliminary work, we analyse the
neighborhood structure of Pareto-optimal spanning trees and design several highly
biased sub-graph-based mutation operators founded on the gained insights. In a
nutshell, these operators replace (un)connected sub-trees of candidate solutions
with locally optimal sub-trees. The latter (biased) step is realized by applying
Kruskal’s single-objective MST algorithm to a weighted sum scalarization of a
sub-graph.We prove runtime complexity results for the introduced operators and
investigate the desirable Pareto-beneficial property. This property states that
mutants cannot be dominated by their parent. Moreover, we perform an extensive
experimental benchmark study to showcase the operator’s practical suitability.
Our results confirm that the subgraph based operators beat baseline algorithms
from the literature even with severely restricted computational budget in terms
of function evaluations on four different classes of complete graphs with different
shapes of the Pareto-front.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: Bossek J, Grimme C. On Single-Objective Sub-Graph-Based Mutation for Solving
the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation.
Published online 2023:1–35. doi:10.1162/evco_a_00335
apa: Bossek, J., & Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation
for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation,
1–35. https://doi.org/10.1162/evco_a_00335
bibtex: '@article{Bossek_Grimme_2023, title={On Single-Objective Sub-Graph-Based
Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem}, DOI={10.1162/evco_a_00335}, journal={Evolutionary
Computation}, author={Bossek, Jakob and Grimme, Christian}, year={2023}, pages={1–35}
}'
chicago: Bossek, Jakob, and Christian Grimme. “On Single-Objective Sub-Graph-Based
Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.” Evolutionary
Computation, 2023, 1–35. https://doi.org/10.1162/evco_a_00335.
ieee: 'J. Bossek and C. Grimme, “On Single-Objective Sub-Graph-Based Mutation for
Solving the Bi-Objective Minimum Spanning Tree Problem,” Evolutionary Computation,
pp. 1–35, 2023, doi: 10.1162/evco_a_00335.'
mla: Bossek, Jakob, and Christian Grimme. “On Single-Objective Sub-Graph-Based Mutation
for Solving the Bi-Objective Minimum Spanning Tree Problem.” Evolutionary Computation,
2023, pp. 1–35, doi:10.1162/evco_a_00335.
short: J. Bossek, C. Grimme, Evolutionary Computation (2023) 1–35.
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:51:42Z
department:
- _id: '819'
doi: 10.1162/evco_a_00335
language:
- iso: eng
page: 1–35
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum
Spanning Tree Problem
type: journal_article
user_id: '102979'
year: '2023'
...
---
_id: '52530'
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. Investigating the Viability of Existing Exploratory
Landscape Analysis Features for Mixed-Integer Problems. In: Silva S, Paquete L,
eds. Companion Proceedings of the Conference on Genetic and Evolutionary Computation,
GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023. ACM; 2023:451–454.
doi:10.1145/3583133.3590757'
apa: Prager, R. P., & Trautmann, H. (2023). Investigating the Viability of Existing
Exploratory Landscape Analysis Features for Mixed-Integer Problems. In S. Silva
& L. Paquete (Eds.), Companion Proceedings of the Conference on Genetic
and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal,
July 15-19, 2023 (pp. 451–454). ACM. https://doi.org/10.1145/3583133.3590757
bibtex: '@inproceedings{Prager_Trautmann_2023, title={Investigating the Viability
of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems},
DOI={10.1145/3583133.3590757},
booktitle={Companion Proceedings of the Conference on Genetic and Evolutionary
Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023},
publisher={ACM}, author={Prager, Raphael Patrick and Trautmann, Heike}, editor={Silva,
Sara and Paquete, Luís}, year={2023}, pages={451–454} }'
chicago: Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability
of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.”
In Companion Proceedings of the Conference on Genetic and Evolutionary Computation,
GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, edited by
Sara Silva and Luís Paquete, 451–454. ACM, 2023. https://doi.org/10.1145/3583133.3590757.
ieee: 'R. P. Prager and H. Trautmann, “Investigating the Viability of Existing Exploratory
Landscape Analysis Features for Mixed-Integer Problems,” in Companion Proceedings
of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion
Volume, Lisbon, Portugal, July 15-19, 2023, 2023, pp. 451–454, doi: 10.1145/3583133.3590757.'
mla: Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability
of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.”
Companion Proceedings of the Conference on Genetic and Evolutionary Computation,
GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, edited by
Sara Silva and Luís Paquete, ACM, 2023, pp. 451–454, doi:10.1145/3583133.3590757.
short: 'R.P. Prager, H. Trautmann, in: S. Silva, L. Paquete (Eds.), Companion Proceedings
of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion
Volume, Lisbon, Portugal, July 15-19, 2023, ACM, 2023, pp. 451–454.'
date_created: 2024-03-13T09:55:17Z
date_updated: 2024-03-13T10:28:07Z
department:
- _id: '819'
doi: 10.1145/3583133.3590757
editor:
- first_name: Sara
full_name: Silva, Sara
last_name: Silva
- first_name: Luís
full_name: Paquete, Luís
last_name: Paquete
language:
- iso: eng
page: 451–454
publication: Companion Proceedings of the Conference on Genetic and Evolutionary Computation,
GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023
publisher: ACM
status: public
title: Investigating the Viability of Existing Exploratory Landscape Analysis Features
for Mixed-Integer Problems
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '52863'
author:
- first_name: Urban
full_name: Ŝkvorc, Urban
last_name: Ŝkvorc
- first_name: Tome
full_name: Eftimov, Tome
last_name: Eftimov
- first_name: Peter
full_name: Koro]ec, Peter
last_name: Koro]ec
citation:
ama: 'Ŝkvorc U, Eftimov T, Koro]ec P. Analyzing the Generalizability of Automated
Algorithm Selection: A Case Study for Numerical Optimization. In: 2023 IEEE
Symposium Series on Computational Intelligence (SSCI). IEEE; 2023. doi:10.1109/ssci52147.2023.10371868'
apa: 'Ŝkvorc, U., Eftimov, T., & Koro]ec, P. (2023). Analyzing the Generalizability
of Automated Algorithm Selection: A Case Study for Numerical Optimization. 2023
IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci52147.2023.10371868'
bibtex: '@inproceedings{Ŝkvorc_Eftimov_Koro]ec_2023, title={Analyzing the Generalizability
of Automated Algorithm Selection: A Case Study for Numerical Optimization}, DOI={10.1109/ssci52147.2023.10371868},
booktitle={2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE},
author={Ŝkvorc, Urban and Eftimov, Tome and Koro]ec, Peter}, year={2023} }'
chicago: 'Ŝkvorc, Urban, Tome Eftimov, and Peter Koro]ec. “Analyzing the Generalizability
of Automated Algorithm Selection: A Case Study for Numerical Optimization.” In
2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE,
2023. https://doi.org/10.1109/ssci52147.2023.10371868.'
ieee: 'U. Ŝkvorc, T. Eftimov, and P. Koro]ec, “Analyzing the Generalizability of
Automated Algorithm Selection: A Case Study for Numerical Optimization,” 2023,
doi: 10.1109/ssci52147.2023.10371868.'
mla: 'Ŝkvorc, Urban, et al. “Analyzing the Generalizability of Automated Algorithm
Selection: A Case Study for Numerical Optimization.” 2023 IEEE Symposium Series
on Computational Intelligence (SSCI), IEEE, 2023, doi:10.1109/ssci52147.2023.10371868.'
short: 'U. Ŝkvorc, T. Eftimov, P. Koro]ec, in: 2023 IEEE Symposium Series on Computational
Intelligence (SSCI), IEEE, 2023.'
date_created: 2024-03-25T14:23:53Z
date_updated: 2024-03-26T10:45:19Z
department:
- _id: '819'
doi: 10.1109/ssci52147.2023.10371868
extern: '1'
language:
- iso: eng
publication: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
publisher: IEEE
status: public
title: 'Analyzing the Generalizability of Automated Algorithm Selection: A Case Study
for Numerical Optimization'
type: conference
user_id: '103764'
year: '2023'
...
---
_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'
...