---
_id: '63703'
author:
- first_name: Tilman
full_name: Hoffbauer, Tilman
last_name: Hoffbauer
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Hoffbauer T, Hoos HH, Bossek J. KernelMatmul: Scaling Gaussian Processes to
Large Time Series. In: Walsh T, Shah J, Kolter Z, eds. AAAI-25, Sponsored by
the Association for the Advancement of Artificial Intelligence, February 25 -
March 4, 2025, Philadelphia, PA, USA. AAAI Press; 2025:17223–17230. doi:10.1609/AAAI.V39I16.33893'
apa: 'Hoffbauer, T., Hoos, H. H., & Bossek, J. (2025). KernelMatmul: Scaling
Gaussian Processes to Large Time Series. In T. Walsh, J. Shah, & Z. Kolter
(Eds.), AAAI-25, Sponsored by the Association for the Advancement of Artificial
Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA (pp. 17223–17230).
AAAI Press. https://doi.org/10.1609/AAAI.V39I16.33893'
bibtex: '@inproceedings{Hoffbauer_Hoos_Bossek_2025, title={KernelMatmul: Scaling
Gaussian Processes to Large Time Series}, DOI={10.1609/AAAI.V39I16.33893},
booktitle={AAAI-25, Sponsored by the Association for the Advancement of Artificial
Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA}, publisher={AAAI
Press}, author={Hoffbauer, Tilman and Hoos, Holger H. and Bossek, Jakob}, editor={Walsh,
Toby and Shah, Julie and Kolter, Zico}, year={2025}, pages={17223–17230} }'
chicago: 'Hoffbauer, Tilman, Holger H. Hoos, and Jakob Bossek. “KernelMatmul: Scaling
Gaussian Processes to Large Time Series.” In AAAI-25, Sponsored by the Association
for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia,
PA, USA, edited by Toby Walsh, Julie Shah, and Zico Kolter, 17223–17230. AAAI
Press, 2025. https://doi.org/10.1609/AAAI.V39I16.33893.'
ieee: 'T. Hoffbauer, H. H. Hoos, and J. Bossek, “KernelMatmul: Scaling Gaussian
Processes to Large Time Series,” in AAAI-25, Sponsored by the Association for
the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia,
PA, USA, 2025, pp. 17223–17230, doi: 10.1609/AAAI.V39I16.33893.'
mla: 'Hoffbauer, Tilman, et al. “KernelMatmul: Scaling Gaussian Processes to Large
Time Series.” AAAI-25, Sponsored by the Association for the Advancement of
Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA,
edited by Toby Walsh et al., AAAI Press, 2025, pp. 17223–17230, doi:10.1609/AAAI.V39I16.33893.'
short: 'T. Hoffbauer, H.H. Hoos, J. Bossek, in: T. Walsh, J. Shah, Z. Kolter (Eds.),
AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence,
February 25 - March 4, 2025, Philadelphia, PA, USA, AAAI Press, 2025, pp. 17223–17230.'
date_created: 2026-01-22T14:42:21Z
date_updated: 2026-01-22T14:45:23Z
department:
- _id: '819'
doi: 10.1609/AAAI.V39I16.33893
editor:
- first_name: Toby
full_name: Walsh, Toby
last_name: Walsh
- first_name: Julie
full_name: Shah, Julie
last_name: Shah
- first_name: Zico
full_name: Kolter, Zico
last_name: Kolter
language:
- iso: eng
page: 17223–17230
publication: AAAI-25, Sponsored by the Association for the Advancement of Artificial
Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA
publisher: AAAI Press
status: public
title: 'KernelMatmul: Scaling Gaussian Processes to Large Time Series'
type: conference
user_id: '15504'
year: '2025'
...
---
_id: '63704'
author:
- first_name: Dominic
full_name: Wittner, Dominic
last_name: Wittner
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Wittner D, Bossek J. Cluster Prevention in Evolutionary Diversity Optimization
for Parallel Machine Scheduling. In: Filipic B, ed. Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga,
Spain, July 14-18, 2025. ACM; 2025:340–348. doi:10.1145/3712256.3726357'
apa: Wittner, D., & Bossek, J. (2025). Cluster Prevention in Evolutionary Diversity
Optimization for Parallel Machine Scheduling. In B. Filipic (Ed.), Proceedings
of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga
Hotel, Malaga, Spain, July 14-18, 2025 (pp. 340–348). ACM. https://doi.org/10.1145/3712256.3726357
bibtex: '@inproceedings{Wittner_Bossek_2025, title={Cluster Prevention in Evolutionary
Diversity Optimization for Parallel Machine Scheduling}, DOI={10.1145/3712256.3726357},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}, publisher={ACM},
author={Wittner, Dominic and Bossek, Jakob}, editor={Filipic, Bogdan}, year={2025},
pages={340–348} }'
chicago: Wittner, Dominic, and Jakob Bossek. “Cluster Prevention in Evolutionary
Diversity Optimization for Parallel Machine Scheduling.” In Proceedings of
the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel,
Malaga, Spain, July 14-18, 2025, edited by Bogdan Filipic, 340–348. ACM, 2025.
https://doi.org/10.1145/3712256.3726357.
ieee: 'D. Wittner and J. Bossek, “Cluster Prevention in Evolutionary Diversity Optimization
for Parallel Machine Scheduling,” in Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18,
2025, 2025, pp. 340–348, doi: 10.1145/3712256.3726357.'
mla: Wittner, Dominic, and Jakob Bossek. “Cluster Prevention in Evolutionary Diversity
Optimization for Parallel Machine Scheduling.” Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain,
July 14-18, 2025, edited by Bogdan Filipic, ACM, 2025, pp. 340–348, doi:10.1145/3712256.3726357.
short: 'D. Wittner, J. Bossek, in: B. Filipic (Ed.), Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga,
Spain, July 14-18, 2025, ACM, 2025, pp. 340–348.'
date_created: 2026-01-22T14:42:45Z
date_updated: 2026-01-22T14:46:04Z
department:
- _id: '819'
doi: 10.1145/3712256.3726357
editor:
- first_name: Bogdan
full_name: Filipic, Bogdan
last_name: Filipic
language:
- iso: eng
page: 340–348
publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025
publisher: ACM
status: public
title: Cluster Prevention in Evolutionary Diversity Optimization for Parallel Machine
Scheduling
type: conference
user_id: '15504'
year: '2025'
...
---
_id: '59073'
author:
- first_name: Jeroen G.
full_name: Rook, Jeroen G.
last_name: Rook
- first_name: Carolin
full_name: Benjamins, Carolin
last_name: Benjamins
- 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
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Marius
full_name: Lindauer, Marius
last_name: Lindauer
citation:
ama: 'Rook JG, Benjamins C, Bossek J, Trautmann H, Hoos HH, Lindauer M. MO-SMAC:
Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary
Computation. Published online 2025:1-25. doi:10.1162/evco_a_00371'
apa: 'Rook, J. G., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H. H., &
Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm
Configuration. Evolutionary Computation, 1–25. https://doi.org/10.1162/evco_a_00371'
bibtex: '@article{Rook_Benjamins_Bossek_Trautmann_Hoos_Lindauer_2025, title={MO-SMAC:
Multi-objective Sequential Model-based Algorithm Configuration}, DOI={10.1162/evco_a_00371},
journal={Evolutionary Computation}, author={Rook, Jeroen G. and Benjamins, Carolin
and Bossek, Jakob and Trautmann, Heike and Hoos, Holger H. and Lindauer, Marius},
year={2025}, pages={1–25} }'
chicago: 'Rook, Jeroen G., Carolin Benjamins, Jakob Bossek, Heike Trautmann, Holger
H. Hoos, and Marius Lindauer. “MO-SMAC: Multi-Objective Sequential Model-Based
Algorithm Configuration.” Evolutionary Computation, 2025, 1–25. https://doi.org/10.1162/evco_a_00371.'
ieee: 'J. G. Rook, C. Benjamins, J. Bossek, H. Trautmann, H. H. Hoos, and M. Lindauer,
“MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration,” Evolutionary
Computation, pp. 1–25, 2025, doi: 10.1162/evco_a_00371.'
mla: 'Rook, Jeroen G., et al. “MO-SMAC: Multi-Objective Sequential Model-Based Algorithm
Configuration.” Evolutionary Computation, 2025, pp. 1–25, doi:10.1162/evco_a_00371.'
short: J.G. Rook, C. Benjamins, J. Bossek, H. Trautmann, H.H. Hoos, M. Lindauer,
Evolutionary Computation (2025) 1–25.
date_created: 2025-03-21T06:24:46Z
date_updated: 2025-03-21T06:25:31Z
doi: 10.1162/evco_a_00371
language:
- iso: eng
page: 1-25
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: 'MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration'
type: journal_article
user_id: '15504'
year: '2025'
...
---
_id: '60812'
author:
- first_name: Oliver Ludger
full_name: Preuß, Oliver Ludger
id: '102978'
last_name: Preuß
orcid: 0009-0008-9308-2418
- first_name: Carolin
full_name: Mensendiek, Carolin
id: '75006'
last_name: Mensendiek
- first_name: Jeroen
full_name: Rook, Jeroen
id: '102977'
last_name: Rook
- 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: 'Preuß OL, Mensendiek C, Rook J, Bossek J, Trautmann H. Automated Algorithm
Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes. In:
Filipic B, ed. Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025. ACM; 2025:58–66.
doi:10.1145/3712256.3726481'
apa: Preuß, O. L., Mensendiek, C., Rook, J., Bossek, J., & Trautmann, H. (2025).
Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous
MNK-Landscapes. In B. Filipic (Ed.), Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18,
2025 (pp. 58–66). ACM. https://doi.org/10.1145/3712256.3726481
bibtex: '@inproceedings{Preuß_Mensendiek_Rook_Bossek_Trautmann_2025, title={Automated
Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes},
DOI={10.1145/3712256.3726481},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}, publisher={ACM},
author={Preuß, Oliver Ludger and Mensendiek, Carolin and Rook, Jeroen and Bossek,
Jakob and Trautmann, Heike}, editor={Filipic, Bogdan}, year={2025}, pages={58–66}
}'
chicago: Preuß, Oliver Ludger, Carolin Mensendiek, Jeroen Rook, Jakob Bossek, and
Heike Trautmann. “Automated Algorithm Configuration and Systematic Benchmarking
for Heterogeneous MNK-Landscapes.” In Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18,
2025, edited by Bogdan Filipic, 58–66. ACM, 2025. https://doi.org/10.1145/3712256.3726481.
ieee: 'O. L. Preuß, C. Mensendiek, J. Rook, J. Bossek, and H. Trautmann, “Automated
Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes,”
in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025, 2025, pp. 58–66, doi:
10.1145/3712256.3726481.'
mla: Preuß, Oliver Ludger, et al. “Automated Algorithm Configuration and Systematic
Benchmarking for Heterogeneous MNK-Landscapes.” Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga,
Spain, July 14-18, 2025, edited by Bogdan Filipic, ACM, 2025, pp. 58–66, doi:10.1145/3712256.3726481.
short: 'O.L. Preuß, C. Mensendiek, J. Rook, J. Bossek, H. Trautmann, in: B. Filipic
(Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025, ACM, 2025, pp. 58–66.'
date_created: 2025-07-29T06:11:51Z
date_updated: 2025-07-29T06:12:28Z
department:
- _id: '819'
doi: 10.1145/3712256.3726481
editor:
- first_name: Bogdan
full_name: Filipic, Bogdan
last_name: Filipic
language:
- iso: eng
page: 58–66
publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025
publisher: ACM
status: public
title: Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous
MNK-Landscapes
type: conference
user_id: '15504'
year: '2025'
...
---
_id: '52749'
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). IEEE; 2024. doi:10.1109/ssci52147.2023.10372008'
apa: Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann,
H. (2024). Using Reinforcement Learning for Per-Instance Algorithm Configuration
on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI).
https://doi.org/10.1109/ssci52147.2023.10372008
bibtex: '@inproceedings{Seiler_Rook_Heins_Preuß_Bossek_Trautmann_2024, 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)}, publisher={IEEE},
author={Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver
Ludger and Bossek, Jakob and Trautmann, Heike}, year={2024} }'
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). IEEE, 2024. 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,” 2024,
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), IEEE, 2024, 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), IEEE, 2024.'
date_created: 2024-03-25T08:15:01Z
date_updated: 2024-06-10T11:59:44Z
department:
- _id: '819'
doi: 10.1109/ssci52147.2023.10372008
language:
- iso: eng
publication: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
publisher: IEEE
status: public
title: Using Reinforcement Learning for Per-Instance Algorithm Configuration on the
TSP
type: conference
user_id: '15504'
year: '2024'
...
---
_id: '63706'
author:
- first_name: Marcus
full_name: Schmidbauer, Marcus
last_name: Schmidbauer
- first_name: Andre
full_name: Opris, Andre
last_name: Opris
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Schmidbauer M, Opris A, Bossek J, Neumann F, Sudholt D. Guiding Quality Diversity
on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean
Conjunctions. 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:10.1145/3638529.3654160'
apa: 'Schmidbauer, M., Opris, A., Bossek, J., Neumann, F., & Sudholt, D. (2024).
Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature
Space by Adding Boolean Conjunctions. 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/10.1145/3638529.3654160'
bibtex: '@inproceedings{Schmidbauer_Opris_Bossek_Neumann_Sudholt_2024, title={Guiding
Quality Diversity on Monotone Submodular Functions: Customising the Feature Space
by Adding Boolean Conjunctions}, DOI={10.1145/3638529.3654160},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024}, publisher={ACM}, author={Schmidbauer,
Marcus and Opris, Andre and Bossek, Jakob and Neumann, Frank and Sudholt, Dirk},
editor={Li, Xiaodong and Handl, Julia}, year={2024} }'
chicago: 'Schmidbauer, Marcus, Andre Opris, Jakob Bossek, Frank Neumann, and Dirk
Sudholt. “Guiding Quality Diversity on Monotone Submodular Functions: Customising
the Feature Space by Adding Boolean Conjunctions.” 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/10.1145/3638529.3654160.'
ieee: 'M. Schmidbauer, A. Opris, J. Bossek, F. Neumann, and D. Sudholt, “Guiding
Quality Diversity on Monotone Submodular Functions: Customising the Feature Space
by Adding Boolean Conjunctions,” in Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024,
2024, doi: 10.1145/3638529.3654160.'
mla: 'Schmidbauer, Marcus, et al. “Guiding Quality Diversity on Monotone Submodular
Functions: Customising the Feature Space by Adding Boolean Conjunctions.” 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:10.1145/3638529.3654160.'
short: 'M. Schmidbauer, A. Opris, J. Bossek, F. Neumann, D. Sudholt, 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: 2026-01-22T14:44:19Z
date_updated: 2026-01-22T14:45:57Z
department:
- _id: '819'
doi: 10.1145/3638529.3654160
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: 'Guiding Quality Diversity on Monotone Submodular Functions: Customising the
Feature Space by Adding Boolean Conjunctions'
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: '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: '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: '48861'
abstract:
- lang: eng
text: Generating instances of different properties is key to algorithm selection
methods that differentiate between the performance of different solvers for a
given combinatorial optimization problem. A wide range of methods using evolutionary
computation techniques has been introduced in recent years. With this paper, we
contribute to this area of research by providing a new approach based on quality
diversity (QD) that is able to explore the whole feature space. QD algorithms
allow to create solutions of high quality within a given feature space by splitting
it up into boxes and improving solution quality within each box. We use our QD
approach for the generation of TSP instances to visualize and analyze the variety
of instances differentiating various TSP solvers and compare it to instances generated
by established approaches from the literature.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality
Diversity. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:10.1145/3512290.3528851'
apa: Bossek, J., & Neumann, F. (2022). Exploring the Feature Space of TSP Instances
Using Quality Diversity. Proceedings of the Genetic and Evolutionary Computation
Conference, 186–194. https://doi.org/10.1145/3512290.3528851
bibtex: '@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO
’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity},
DOI={10.1145/3512290.3528851},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }'
chicago: 'Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP
Instances Using Quality Diversity.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 186–194. GECCO ’22. New York, NY, USA: Association
for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528851.'
ieee: 'J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using
Quality Diversity,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2022, pp. 186–194, doi: 10.1145/3512290.3528851.'
mla: Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances
Using Quality Diversity.” Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, 2022, pp. 186–194, doi:10.1145/3512290.3528851.
short: 'J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp.
186–194.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:56Z
department:
- _id: '819'
doi: 10.1145/3512290.3528851
extern: '1'
keyword:
- instance features
- instance generation
- quality diversity
- TSP
language:
- iso: eng
page: 186–194
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-9237-2
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’22
status: public
title: Exploring the Feature Space of TSP Instances Using Quality Diversity
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48868'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann A, Neumann F. Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA. In: Proceedings of the Genetic
and Evolutionary Computation Conference Companion. GECCO’22. Association for
Computing Machinery; 2022:824–842. doi:10.1145/3520304.3533626'
apa: 'Bossek, J., Neumann, A., & Neumann, F. (2022). Evolutionary Diversity
Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.
Proceedings of the Genetic and Evolutionary Computation Conference Companion,
824–842. https://doi.org/10.1145/3520304.3533626'
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2022, place={New York, NY, USA},
series={GECCO’22}, title={Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA}, DOI={10.1145/3520304.3533626},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob
and Neumann, Aneta and Neumann, Frank}, year={2022}, pages={824–842}, collection={GECCO’22}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Evolutionary Diversity
Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.”
In Proceedings of the Genetic and Evolutionary Computation Conference Companion,
824–842. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022.
https://doi.org/10.1145/3520304.3533626.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization
for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 2022, pp.
824–842, doi: 10.1145/3520304.3533626.'
mla: 'Bossek, Jakob, et al. “Evolutionary Diversity Optimization for Combinatorial
Optimization: Tutorial at GECCO’22, Boston, USA.” Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing
Machinery, 2022, pp. 824–842, doi:10.1145/3520304.3533626.'
short: 'J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary
Computation Conference Companion, Association for Computing Machinery, New York,
NY, USA, 2022, pp. 824–842.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:19Z
department:
- _id: '819'
doi: 10.1145/3520304.3533626
extern: '1'
language:
- iso: eng
page: 824–842
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-9268-6
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO’22
status: public
title: 'Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial
at GECCO’22, Boston, USA'
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48882'
abstract:
- lang: eng
text: In multimodal multi-objective optimization (MMMOO), the focus is not solely
on convergence in objective space, but rather also on explicitly ensuring diversity
in decision space. We illustrate why commonly used diversity measures are not
entirely appropriate for this task and propose a sophisticated basin-based evaluation
(BBE) method. Also, BBE variants are developed, capturing the anytime behavior
of algorithms. The set of BBE measures is tested by means of an algorithm configuration
study. We show that these new measures also transfer properties of the well-established
hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective
space convergence. Moreover, we advance MMMOO research by providing insights into
the multimodal performance of the considered algorithms. Specifically, algorithms
exploiting local structures are shown to outperform classical evolutionary multi-objective
optimizers regarding the BBE variants and respective trade-off with HV.
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jeroen
full_name: Rook, Jeroen
last_name: Rook
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based
Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G,
Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tusar T, eds. Parallel Problem
Solving from Nature (PPSN XVII). Lecture Notes in Computer Science. Springer
International Publishing; 2022:192–206. doi:10.1007/978-3-031-14714-2_14'
apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann,
H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization
Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &
T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp.
192–206). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_14'
bibtex: '@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022,
place={Cham}, series={Lecture Notes in Computer Science}, title={BBE: Basin-Based
Evaluation of Multimodal Multi-objective Optimization Problems}, DOI={10.1007/978-3-031-14714-2_14},
booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer
International Publishing}, author={Heins, Jonathan and Rook, Jeroen and Schäpermeier,
Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph,
Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa,
Gabriela and Tusar, Tea}, year={2022}, pages={192–206}, collection={Lecture Notes
in Computer Science} }'
chicago: 'Heins, Jonathan, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob
Bossek, and Heike Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective
Optimization Problems.” In Parallel Problem Solving from Nature (PPSN XVII),
edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela
Ochoa, and Tea Tusar, 192–206. Lecture Notes in Computer Science. Cham: Springer
International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_14.'
ieee: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann,
“BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,”
in Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 192–206,
doi: 10.1007/978-3-031-14714-2_14.'
mla: 'Heins, Jonathan, et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective
Optimization Problems.” Parallel Problem Solving from Nature (PPSN XVII),
edited by Günter Rudolph et al., Springer International Publishing, 2022, pp.
192–206, doi:10.1007/978-3-031-14714-2_14.'
short: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann,
in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.),
Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing,
Cham, 2022, pp. 192–206.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:50Z
department:
- _id: '819'
doi: 10.1007/978-3-031-14714-2_14
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
extern: '1'
keyword:
- Anytime behavior
- Benchmarking
- Continuous optimization
- Multi-objective optimization
- Multimodality
- Performance metric
language:
- iso: eng
page: 192–206
place: Cham
publication: Parallel Problem Solving from Nature (PPSN XVII)
publication_identifier:
isbn:
- 978-3-031-14714-2
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems'
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48894'
abstract:
- lang: eng
text: Recently different evolutionary computation approaches have been developed
that generate sets of high quality diverse solutions for a given optimisation
problem. Many studies have considered diversity 1) as a mean to explore niches
in behavioural space (quality diversity) or 2) to increase the structural differences
of solutions (evolutionary diversity optimisation). In this study, we introduce
a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component
traveling thief problem. The results show the capability of the co-evolutionary
algorithm to achieve significantly higher diversity compared to the baseline evolutionary
diversity algorithms from the literature.
author:
- first_name: Adel
full_name: Nikfarjam, Adel
last_name: Nikfarjam
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation
for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke
P, Ochoa G, Tu\v sar T, eds. Parallel Problem Solving from Nature (PPSN XVII).
Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249.
doi:10.1007/978-3-031-14714-2_17'
apa: Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-Evolutionary
Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, & T. Tu\v sar (Eds.), Parallel Problem
Solving from Nature (PPSN XVII) (pp. 237–249). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_17
bibtex: '@inproceedings{Nikfarjam_Neumann_Bossek_Neumann_2022, place={Cham}, series={Lecture
Notes in Computer Science}, title={Co-Evolutionary Diversity Optimisation for
the Traveling Thief Problem}, DOI={10.1007/978-3-031-14714-2_17},
booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer
International Publishing}, author={Nikfarjam, Adel and Neumann, Aneta and Bossek,
Jakob and Neumann, Frank}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre,
Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}, year={2022},
pages={237–249}, collection={Lecture Notes in Computer Science} }'
chicago: 'Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary
Diversity Optimisation for the Traveling Thief Problem.” In Parallel Problem
Solving from Nature (PPSN XVII), edited by Günter Rudolph, Anna V. Kononova,
Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tu\v sar, 237–249. Lecture
Notes in Computer Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_17.'
ieee: 'A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity
Optimisation for the Traveling Thief Problem,” in Parallel Problem Solving
from Nature (PPSN XVII), 2022, pp. 237–249, doi: 10.1007/978-3-031-14714-2_17.'
mla: Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling
Thief Problem.” Parallel Problem Solving from Nature (PPSN XVII), edited
by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249,
doi:10.1007/978-3-031-14714-2_17.
short: 'A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova,
H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\v sar (Eds.), Parallel Problem Solving
from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:51Z
department:
- _id: '819'
doi: 10.1007/978-3-031-14714-2_17
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Hernán
full_name: Aguirre, Hernán
last_name: Aguirre
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Gabriela
full_name: Ochoa, Gabriela
last_name: Ochoa
- first_name: Tea
full_name: Tu\v sar, Tea
last_name: Tu\v sar
extern: '1'
keyword:
- Co-evolutionary algorithms
- Evolutionary diversity optimisation
- Quality diversity
- Traveling thief problem
language:
- iso: eng
page: 237–249
place: Cham
publication: Parallel Problem Solving from Nature (PPSN XVII)
publication_identifier:
isbn:
- 978-3-031-14714-2
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
type: conference
user_id: '102979'
year: '2022'
...
---
_id: '48878'
abstract:
- lang: eng
text: Due to the rise of continuous data-generating applications, analyzing data
streams has gained increasing attention over the past decades. A core research
area in stream data is stream classification, which categorizes or detects data
points within an evolving stream of observations. Areas of stream classification
are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing
a wide range of (social) media applications. Research in stream classification
is related to developing methods that adapt to the changing and potentially volatile
data stream. It focuses on individual aspects of the stream classification pipeline,
e.g., designing suitable algorithm architectures, an efficient train and test
procedure, or detecting so-called concept drifts. As a result of the many different
research questions and strands, the field is challenging to grasp, especially
for beginners. This survey explores, summarizes, and categorizes work within the
domain of stream classification and identifies core research threads over the
past few years. It is structured based on the stream classification process to
facilitate coordination within this complex topic, including common application
scenarios and benchmarking data sets. Thus, both newcomers to the field and experts
who want to widen their scope can gain (additional) insight into this research
area and find starting points and pointers to more in-depth literature on specific
issues and research directions in the field.
author:
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(18):9094.
doi:10.3390/app12189094'
apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review. Applied
Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094'
bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094},
number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital
Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek,
Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }'
chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.'
ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
Stream Classification Pipeline: A Literature Review,” Applied Sciences,
vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.'
mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital
Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.'
short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
12 (2022) 9094.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:50:56Z
department:
- _id: '819'
doi: 10.3390/app12189094
intvolume: ' 12'
issue: '18'
keyword:
- big data
- data mining
- data stream analysis
- machine learning
- stream classification
- supervised learning
language:
- iso: eng
page: '9094'
publication: Applied Sciences
publication_identifier:
issn:
- 2076-3417
publisher: '{Multidisciplinary Digital Publishing Institute}'
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '102979'
volume: 12
year: '2022'
...
---
_id: '48896'
abstract:
- lang: eng
text: Hardness of Multi-Objective (MO) continuous optimization problems results
from an interplay of various problem characteristics, e. g. the degree of multi-modality.
We present a benchmark study of classical and diversity focused optimizers on
multi-modal MO problems based on automated algorithm configuration. We show the
large effect of the latter and investigate the trade-off between convergence in
objective space and diversity in decision space.
author:
- first_name: Jeroen
full_name: Rook, Jeroen
last_name: Rook
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm
Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Proceedings
of the Genetic and Evolutionary Computation Conference Companion. GECCO’22.
Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998'
apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential
of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization
Problems. Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 356–359. https://doi.org/10.1145/3520304.3528998
bibtex: '@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY,
USA}, series={GECCO’22}, title={On the Potential of Automated Algorithm Configuration
on Multi-Modal Multi-Objective Optimization Problems}, DOI={10.1145/3520304.3528998},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Rook, Jeroen
and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}, year={2022}, pages={356–359},
collection={GECCO’22} }'
chicago: 'Rook, Jeroen, Heike Trautmann, Jakob Bossek, and Christian Grimme. “On
the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimization Problems.” In Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 356–359. GECCO’22. New York, NY, USA: Association for
Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.'
ieee: 'J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated
Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,”
in Proceedings of the Genetic and Evolutionary Computation Conference Companion,
2022, pp. 356–359, doi: 10.1145/3520304.3528998.'
mla: Rook, Jeroen, et al. “On the Potential of Automated Algorithm Configuration
on Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing
Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998.
short: 'J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic
and Evolutionary Computation Conference Companion, Association for Computing Machinery,
New York, NY, USA, 2022, pp. 356–359.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:50:24Z
department:
- _id: '819'
doi: 10.1145/3520304.3528998
extern: '1'
keyword:
- configuration
- multi-modality
- multi-objective optimization
language:
- iso: eng
page: 356–359
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-9268-6
publisher: Association for Computing Machinery
series_title: GECCO’22
status: public
title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimization Problems
type: conference
user_id: '102979'
year: '2022'
...