---
_id: '58335'
author:
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Urban
full_name: Skvorc, Urban
id: '103764'
last_name: Skvorc
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Seiler M, Skvorc U, Doerr C, Trautmann H. Synergies of Deep and Classical
Exploratory Landscape Features for Automated Algorithm Selection. In: Festa P,
Ferone D, Pastore T, Pisacane O, eds. Learning and Intelligent Optimization
- 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024,
Revised Selected Papers. Vol 14990. Lecture Notes in Computer Science. Springer;
2024:361–376. doi:10.1007/978-3-031-75623-8_29'
apa: Seiler, M., Skvorc, U., Doerr, C., & Trautmann, H. (2024). Synergies of
Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection.
In P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Eds.), Learning and
Intelligent Optimization - 18th International Conference, LION 18, Ischia Island,
Italy, June 9-13, 2024, Revised Selected Papers (Vol. 14990, pp. 361–376).
Springer. https://doi.org/10.1007/978-3-031-75623-8_29
bibtex: '@inproceedings{Seiler_Skvorc_Doerr_Trautmann_2024, series={Lecture Notes
in Computer Science}, title={Synergies of Deep and Classical Exploratory Landscape
Features for Automated Algorithm Selection}, volume={14990}, DOI={10.1007/978-3-031-75623-8_29},
booktitle={Learning and Intelligent Optimization - 18th International Conference,
LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers}, publisher={Springer},
author={Seiler, Moritz and Skvorc, Urban and Doerr, Carola and Trautmann, Heike},
editor={Festa, Paola and Ferone, Daniele and Pastore, Tommaso and Pisacane, Ornella},
year={2024}, pages={361–376}, collection={Lecture Notes in Computer Science} }'
chicago: Seiler, Moritz, Urban Skvorc, Carola Doerr, and Heike Trautmann. “Synergies
of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection.”
In Learning and Intelligent Optimization - 18th International Conference, LION
18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, edited
by Paola Festa, Daniele Ferone, Tommaso Pastore, and Ornella Pisacane, 14990:361–376.
Lecture Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-75623-8_29.
ieee: 'M. Seiler, U. Skvorc, C. Doerr, and H. Trautmann, “Synergies of Deep and
Classical Exploratory Landscape Features for Automated Algorithm Selection,” in
Learning and Intelligent Optimization - 18th International Conference, LION
18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, 2024,
vol. 14990, pp. 361–376, doi: 10.1007/978-3-031-75623-8_29.'
mla: Seiler, Moritz, et al. “Synergies of Deep and Classical Exploratory Landscape
Features for Automated Algorithm Selection.” Learning and Intelligent Optimization
- 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024,
Revised Selected Papers, edited by Paola Festa et al., vol. 14990, Springer,
2024, pp. 361–376, doi:10.1007/978-3-031-75623-8_29.
short: 'M. Seiler, U. Skvorc, C. Doerr, H. Trautmann, in: P. Festa, D. Ferone, T.
Pastore, O. Pisacane (Eds.), Learning and Intelligent Optimization - 18th International
Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers,
Springer, 2024, pp. 361–376.'
date_created: 2025-01-23T12:39:37Z
date_updated: 2025-01-23T12:40:32Z
department:
- _id: '819'
doi: 10.1007/978-3-031-75623-8_29
editor:
- first_name: Paola
full_name: Festa, Paola
last_name: Festa
- first_name: Daniele
full_name: Ferone, Daniele
last_name: Ferone
- first_name: Tommaso
full_name: Pastore, Tommaso
last_name: Pastore
- first_name: Ornella
full_name: Pisacane, Ornella
last_name: Pisacane
intvolume: ' 14990'
language:
- iso: eng
page: 361–376
publication: Learning and Intelligent Optimization - 18th International Conference,
LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Synergies of Deep and Classical Exploratory Landscape Features for Automated
Algorithm Selection
type: conference
user_id: '15504'
volume: 14990
year: '2024'
...
---
_id: '58338'
citation:
ama: Affenzeller M, Winkler SM, Kononova AV, et al., eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part III. Vol 15150. Springer;
2024. doi:10.1007/978-3-031-70071-2
apa: Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tusar, T.,
Machado, P., & Bäck, T. (Eds.). (2024). Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part III (Vol. 15150). Springer. https://doi.org/10.1007/978-3-031-70071-2
bibtex: '@book{Affenzeller_Winkler_Kononova_Trautmann_Tusar_Machado_Bäck_2024, series={Lecture
Notes in Computer Science}, title={Parallel Problem Solving from Nature - PPSN
XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part III}, volume={15150}, DOI={10.1007/978-3-031-70071-2},
publisher={Springer}, year={2024}, collection={Lecture Notes in Computer Science}
}'
chicago: Affenzeller, Michael, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann,
Tea Tusar, Penousal Machado, and Thomas Bäck, eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part III. Vol. 15150. Lecture
Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70071-2.
ieee: M. Affenzeller et al., Eds., Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part III, vol. 15150. Springer, 2024.
mla: Affenzeller, Michael, et al., editors. Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part III. Springer, 2024, doi:10.1007/978-3-031-70071-2.
short: M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado,
T. Bäck, eds., Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part III, Springer, 2024.
date_created: 2025-01-23T12:42:54Z
date_updated: 2025-01-23T12:44:21Z
department:
- _id: '819'
doi: 10.1007/978-3-031-70071-2
editor:
- first_name: Michael
full_name: Affenzeller, Michael
last_name: Affenzeller
- first_name: Stephan M.
full_name: Winkler, Stephan M.
last_name: Winkler
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
- first_name: Penousal
full_name: Machado, Penousal
last_name: Machado
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
intvolume: ' 15150'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-031-70070-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part III
type: conference_editor
user_id: '15504'
volume: 15150
year: '2024'
...
---
_id: '58336'
citation:
ama: Affenzeller M, Winkler SM, Kononova AV, et al., eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part I. Vol 15148. Springer;
2024. doi:10.1007/978-3-031-70055-2
apa: Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tusar, T.,
Machado, P., & Bäck, T. (Eds.). (2024). Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part I (Vol. 15148). Springer. https://doi.org/10.1007/978-3-031-70055-2
bibtex: '@book{Affenzeller_Winkler_Kononova_Trautmann_Tusar_Machado_Bäck_2024, series={Lecture
Notes in Computer Science}, title={Parallel Problem Solving from Nature - PPSN
XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part I}, volume={15148}, DOI={10.1007/978-3-031-70055-2},
publisher={Springer}, year={2024}, collection={Lecture Notes in Computer Science}
}'
chicago: Affenzeller, Michael, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann,
Tea Tusar, Penousal Machado, and Thomas Bäck, eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part I. Vol. 15148. Lecture Notes
in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70055-2.
ieee: M. Affenzeller et al., Eds., Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part I, vol. 15148. Springer, 2024.
mla: Affenzeller, Michael, et al., editors. Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part I. Springer, 2024, doi:10.1007/978-3-031-70055-2.
short: M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado,
T. Bäck, eds., Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part I, Springer, 2024.
date_created: 2025-01-23T12:41:21Z
date_updated: 2025-01-23T12:44:14Z
department:
- _id: '819'
doi: 10.1007/978-3-031-70055-2
editor:
- first_name: Michael
full_name: Affenzeller, Michael
last_name: Affenzeller
- first_name: Stephan M.
full_name: Winkler, Stephan M.
last_name: Winkler
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
- first_name: Penousal
full_name: Machado, Penousal
last_name: Machado
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
intvolume: ' 15148'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-031-70054-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part I
type: conference_editor
user_id: '15504'
volume: 15148
year: '2024'
...
---
_id: '58339'
citation:
ama: Affenzeller M, Winkler SM, Kononova AV, et al., eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part IV. Vol 15151. Springer;
2024. doi:10.1007/978-3-031-70085-9
apa: Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tusar, T.,
Machado, P., & Bäck, T. (Eds.). (2024). Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part IV (Vol. 15151). Springer. https://doi.org/10.1007/978-3-031-70085-9
bibtex: '@book{Affenzeller_Winkler_Kononova_Trautmann_Tusar_Machado_Bäck_2024, series={Lecture
Notes in Computer Science}, title={Parallel Problem Solving from Nature - PPSN
XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part IV}, volume={15151}, DOI={10.1007/978-3-031-70085-9},
publisher={Springer}, year={2024}, collection={Lecture Notes in Computer Science}
}'
chicago: Affenzeller, Michael, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann,
Tea Tusar, Penousal Machado, and Thomas Bäck, eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part IV. Vol. 15151. Lecture
Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70085-9.
ieee: M. Affenzeller et al., Eds., Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part IV, vol. 15151. Springer, 2024.
mla: Affenzeller, Michael, et al., editors. Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part IV. Springer, 2024, doi:10.1007/978-3-031-70085-9.
short: M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado,
T. Bäck, eds., Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part IV, Springer, 2024.
date_created: 2025-01-23T12:43:27Z
date_updated: 2025-01-23T12:44:17Z
department:
- _id: '819'
doi: 10.1007/978-3-031-70085-9
editor:
- first_name: Michael
full_name: Affenzeller, Michael
last_name: Affenzeller
- first_name: Stephan M.
full_name: Winkler, Stephan M.
last_name: Winkler
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
- first_name: Penousal
full_name: Machado, Penousal
last_name: Machado
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
intvolume: ' 15151'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-031-70084-2
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part IV
type: conference_editor
user_id: '15504'
volume: 15151
year: '2024'
...
---
_id: '58337'
citation:
ama: Affenzeller M, Winkler SM, Kononova AV, et al., eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part II. Vol 15149. Springer;
2024. doi:10.1007/978-3-031-70068-2
apa: Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tusar, T.,
Machado, P., & Bäck, T. (Eds.). (2024). Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part II (Vol. 15149). Springer. https://doi.org/10.1007/978-3-031-70068-2
bibtex: '@book{Affenzeller_Winkler_Kononova_Trautmann_Tusar_Machado_Bäck_2024, series={Lecture
Notes in Computer Science}, title={Parallel Problem Solving from Nature - PPSN
XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part II}, volume={15149}, DOI={10.1007/978-3-031-70068-2},
publisher={Springer}, year={2024}, collection={Lecture Notes in Computer Science}
}'
chicago: Affenzeller, Michael, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann,
Tea Tusar, Penousal Machado, and Thomas Bäck, eds. Parallel Problem Solving
from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg,
Austria, September 14-18, 2024, Proceedings, Part II. Vol. 15149. Lecture
Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70068-2.
ieee: M. Affenzeller et al., Eds., Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part II, vol. 15149. Springer, 2024.
mla: Affenzeller, Michael, et al., editors. Parallel Problem Solving from Nature
- PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September
14-18, 2024, Proceedings, Part II. Springer, 2024, doi:10.1007/978-3-031-70068-2.
short: M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado,
T. Bäck, eds., Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part II, Springer, 2024.
date_created: 2025-01-23T12:42:15Z
date_updated: 2025-01-23T12:44:24Z
doi: 10.1007/978-3-031-70068-2
editor:
- first_name: Michael
full_name: Affenzeller, Michael
last_name: Affenzeller
- first_name: Stephan M.
full_name: Winkler, Stephan M.
last_name: Winkler
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
- first_name: Penousal
full_name: Machado, Penousal
last_name: Machado
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
intvolume: ' 15149'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-031-70067-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II
type: conference_editor
user_id: '15504'
volume: 15149
year: '2024'
...
---
_id: '60131'
author:
- first_name: Oliver Ludger
full_name: Preuß, Oliver Ludger
id: '102978'
last_name: Preuß
orcid: 0009-0008-9308-2418
- first_name: Jeroen
full_name: Rook, Jeroen
id: '102977'
last_name: Rook
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Preuß OL, Rook J, Trautmann H. On the Potential of Multi-objective Automated
Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems.
In: Smith SL, Correia J, Cintrano C, eds. Applications of Evolutionary Computation
- 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024,
Aberystwyth, UK, April 3-5, 2024, Proceedings, Part I. Vol 14634. Lecture
Notes in Computer Science. Springer; 2024:305–321. doi:10.1007/978-3-031-56852-7_20'
apa: Preuß, O. L., Rook, J., & Trautmann, H. (2024). On the Potential of Multi-objective
Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation
Problems. In S. L. Smith, J. Correia, & C. Cintrano (Eds.), Applications
of Evolutionary Computation - 27th European Conference, EvoApplications 2024,
Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings, Part
I (Vol. 14634, pp. 305–321). Springer. https://doi.org/10.1007/978-3-031-56852-7_20
bibtex: '@inproceedings{Preuß_Rook_Trautmann_2024, series={Lecture Notes in Computer
Science}, title={On the Potential of Multi-objective Automated Algorithm Configuration
on Multi-modal Multi-objective Optimisation Problems}, volume={14634}, DOI={10.1007/978-3-031-56852-7_20},
booktitle={Applications of Evolutionary Computation - 27th European Conference,
EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5,
2024, Proceedings, Part I}, publisher={Springer}, author={Preuß, Oliver Ludger
and Rook, Jeroen and Trautmann, Heike}, editor={Smith, Stephen L. and Correia,
João and Cintrano, Christian}, year={2024}, pages={305–321}, collection={Lecture
Notes in Computer Science} }'
chicago: Preuß, Oliver Ludger, Jeroen Rook, and Heike Trautmann. “On the Potential
of Multi-Objective Automated Algorithm Configuration on Multi-Modal Multi-Objective
Optimisation Problems.” In Applications of Evolutionary Computation - 27th
European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth,
UK, April 3-5, 2024, Proceedings, Part I, edited by Stephen L. Smith, João
Correia, and Christian Cintrano, 14634:305–321. Lecture Notes in Computer Science.
Springer, 2024. https://doi.org/10.1007/978-3-031-56852-7_20.
ieee: 'O. L. Preuß, J. Rook, and H. Trautmann, “On the Potential of Multi-objective
Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation
Problems,” in Applications of Evolutionary Computation - 27th European Conference,
EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5,
2024, Proceedings, Part I, 2024, vol. 14634, pp. 305–321, doi: 10.1007/978-3-031-56852-7_20.'
mla: Preuß, Oliver Ludger, et al. “On the Potential of Multi-Objective Automated
Algorithm Configuration on Multi-Modal Multi-Objective Optimisation Problems.”
Applications of Evolutionary Computation - 27th European Conference, EvoApplications
2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings,
Part I, edited by Stephen L. Smith et al., vol. 14634, Springer, 2024, pp.
305–321, doi:10.1007/978-3-031-56852-7_20.
short: 'O.L. Preuß, J. Rook, H. Trautmann, in: S.L. Smith, J. Correia, C. Cintrano
(Eds.), Applications of Evolutionary Computation - 27th European Conference, EvoApplications
2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings,
Part I, Springer, 2024, pp. 305–321.'
date_created: 2025-06-04T12:47:35Z
date_updated: 2025-06-05T06:01:02Z
doi: 10.1007/978-3-031-56852-7_20
editor:
- first_name: Stephen L.
full_name: Smith, Stephen L.
last_name: Smith
- first_name: João
full_name: Correia, João
last_name: Correia
- first_name: Christian
full_name: Cintrano, Christian
last_name: Cintrano
intvolume: ' 14634'
language:
- iso: eng
page: 305–321
publication: Applications of Evolutionary Computation - 27th European Conference,
EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5,
2024, Proceedings, Part I
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal
Multi-objective Optimisation Problems
type: conference
user_id: '15504'
volume: 14634
year: '2024'
...
---
_id: '60132'
author:
- first_name: Moritz
full_name: Seiler, Moritz
id: '105520'
last_name: Seiler
- first_name: Urban
full_name: Skvorc, Urban
id: '103764'
last_name: Skvorc
- first_name: Gjorgjina
full_name: Cenikj, Gjorgjina
last_name: Cenikj
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Seiler M, Skvorc U, Cenikj G, Doerr C, Trautmann H. Learned Features vs. Classical
ELA on Affine BBOB Functions. In: Affenzeller M, Winkler SM, Kononova AV, et al.,
eds. Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part II. Vol 15149. Lecture Notes in Computer Science. Springer; 2024:137–153.
doi:10.1007/978-3-031-70068-2_9'
apa: Seiler, M., Skvorc, U., Cenikj, G., Doerr, C., & Trautmann, H. (2024).
Learned Features vs. Classical ELA on Affine BBOB Functions. In M. Affenzeller,
S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tusar, P. Machado, & T. Bäck
(Eds.), Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part II (Vol. 15149, pp. 137–153). Springer. https://doi.org/10.1007/978-3-031-70068-2_9
bibtex: '@inproceedings{Seiler_Skvorc_Cenikj_Doerr_Trautmann_2024, series={Lecture
Notes in Computer Science}, title={Learned Features vs. Classical ELA on Affine
BBOB Functions}, volume={15149}, DOI={10.1007/978-3-031-70068-2_9},
booktitle={Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings,
Part II}, publisher={Springer}, author={Seiler, Moritz and Skvorc, Urban and Cenikj,
Gjorgjina and Doerr, Carola and Trautmann, Heike}, editor={Affenzeller, Michael
and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar,
Tea and Machado, Penousal and Bäck, Thomas}, year={2024}, pages={137–153}, collection={Lecture
Notes in Computer Science} }'
chicago: Seiler, Moritz, Urban Skvorc, Gjorgjina Cenikj, Carola Doerr, and Heike
Trautmann. “Learned Features vs. Classical ELA on Affine BBOB Functions.” In Parallel
Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN
2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, edited
by Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann,
Tea Tusar, Penousal Machado, and Thomas Bäck, 15149:137–153. Lecture Notes in
Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70068-2_9.
ieee: 'M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features
vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from
Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria,
September 14-18, 2024, Proceedings, Part II, 2024, vol. 15149, pp. 137–153,
doi: 10.1007/978-3-031-70068-2_9.'
mla: Seiler, Moritz, et al. “Learned Features vs. Classical ELA on Affine BBOB Functions.”
Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II,
edited by Michael Affenzeller et al., vol. 15149, Springer, 2024, pp. 137–153,
doi:10.1007/978-3-031-70068-2_9.
short: 'M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann, in: M. Affenzeller,
S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado, T. Bäck (Eds.),
Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference,
PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, Springer,
2024, pp. 137–153.'
date_created: 2025-06-04T12:48:56Z
date_updated: 2025-06-04T12:49:30Z
doi: 10.1007/978-3-031-70068-2_9
editor:
- first_name: Michael
full_name: Affenzeller, Michael
last_name: Affenzeller
- first_name: Stephan M.
full_name: Winkler, Stephan M.
last_name: Winkler
- first_name: Anna V.
full_name: Kononova, Anna V.
last_name: Kononova
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Tea
full_name: Tusar, Tea
last_name: Tusar
- first_name: Penousal
full_name: Machado, Penousal
last_name: Machado
- first_name: Thomas
full_name: Bäck, Thomas
last_name: Bäck
intvolume: ' 15149'
language:
- iso: eng
page: 137–153
publication: Parallel Problem Solving from Nature - PPSN XVIII - 18th International
Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part
II
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Learned Features vs. Classical ELA on Affine BBOB Functions
type: conference
user_id: '15504'
volume: 15149
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: '59283'
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. Evol Comput. 2024;32(3):211–216.
doi:10.1162/EVCO_A_00341'
apa: 'Prager, R. P., & Trautmann, H. (2024). Pflacco: Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems in Python. Evol.
Comput., 32(3), 211–216. https://doi.org/10.1162/EVCO_A_00341'
bibtex: '@article{Prager_Trautmann_2024, title={Pflacco: Feature-Based Landscape
Analysis of Continuous and Constrained Optimization Problems in Python}, volume={32},
DOI={10.1162/EVCO_A_00341},
number={3}, journal={Evol. Comput.}, author={Prager, Raphael Patrick and Trautmann,
Heike}, year={2024}, pages={211–216} }'
chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based
Landscape Analysis of Continuous and Constrained Optimization Problems in Python.”
Evol. Comput. 32, no. 3 (2024): 211–216. 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,” Evol. Comput.,
vol. 32, no. 3, pp. 211–216, 2024, 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.” Evol.
Comput., vol. 32, no. 3, 2024, pp. 211–216, doi:10.1162/EVCO_A_00341.'
short: R.P. Prager, H. Trautmann, Evol. Comput. 32 (2024) 211–216.
date_created: 2025-04-03T05:56:07Z
date_updated: 2025-04-03T05:56:33Z
doi: 10.1162/EVCO_A_00341
intvolume: ' 32'
issue: '3'
language:
- iso: eng
page: 211–216
publication: Evol. Comput.
status: public
title: 'Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization
Problems in Python'
type: journal_article
user_id: '15504'
volume: 32
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'
...