---
_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: '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: '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: '48884'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
on a large set of well-known benchmark instances and demonstrate complementary
performance, in that different instances may be solved most effectively by different
algorithms. We leverage this complementarity to build an algorithm selector, which
selects the best TSP solver on a per-instance basis and thus achieves significantly
improved performance compared to the single best solver, representing an advance
in the state of the art in solving the Euclidean TSP. Our in-depth analysis of
the selectors provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620.
doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff,
Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018},
pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation,
vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...
---
_id: '46418'
abstract:
- lang: eng
text: In this paper, two approaches for estimating the generation in which a multi-objective
evolutionary algorithm (MOEA) shows statistically significant signs of convergence
are introduced. A set-based perspective is taken where convergence is measured
by performance indicators. The proposed techniques fulfill the requirements of
proper statistical assessment on the one hand and efficient optimisation for real-world
problems on the other hand. The first approach accounts for the stochastic nature
of the MOEA by repeating the optimisation runs for increasing generation numbers
and analysing the performance indicators using statistical tools. This technique
results in a very robust offline procedure. Moreover, an online convergence detection
method is introduced as well. This method automatically stops the MOEA when either
the variance of the performance indicators falls below a specified threshold or
a stagnation of their overall trend is detected. Both methods are analysed and
compared for two MOEA and on different classes of benchmark functions. It is shown
that the methods successfully operate on all stated problems needing less function
evaluations while preserving good approximation quality at the same time.
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: T.
full_name: Wagner, T.
last_name: Wagner
- first_name: B.
full_name: Naujoks, B.
last_name: Naujoks
- first_name: M.
full_name: Preuss, M.
last_name: Preuss
- first_name: J.
full_name: Mehnen, J.
last_name: Mehnen
citation:
ama: Trautmann H, Wagner T, Naujoks B, Preuss M, Mehnen J. Statistical Methods for
Convergence Detection of Multi-Objective Evolutionary Algorithms. Evolutionary
Computation. 2009;17(4):493-509. doi:10.1162/evco.2009.17.4.17403
apa: Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., & Mehnen, J. (2009).
Statistical Methods for Convergence Detection of Multi-Objective Evolutionary
Algorithms. Evolutionary Computation, 17(4), 493–509. https://doi.org/10.1162/evco.2009.17.4.17403
bibtex: '@article{Trautmann_Wagner_Naujoks_Preuss_Mehnen_2009, title={Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms},
volume={17}, DOI={10.1162/evco.2009.17.4.17403},
number={4}, journal={Evolutionary Computation}, author={Trautmann, Heike and Wagner,
T. and Naujoks, B. and Preuss, M. and Mehnen, J.}, year={2009}, pages={493–509}
}'
chicago: 'Trautmann, Heike, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen. “Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.”
Evolutionary Computation 17, no. 4 (2009): 493–509. https://doi.org/10.1162/evco.2009.17.4.17403.'
ieee: 'H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen, “Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms,”
Evolutionary Computation, vol. 17, no. 4, pp. 493–509, 2009, doi: 10.1162/evco.2009.17.4.17403.'
mla: Trautmann, Heike, et al. “Statistical Methods for Convergence Detection of
Multi-Objective Evolutionary Algorithms.” Evolutionary Computation, vol.
17, no. 4, 2009, pp. 493–509, doi:10.1162/evco.2009.17.4.17403.
short: H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, J. Mehnen, Evolutionary Computation
17 (2009) 493–509.
date_created: 2023-08-04T16:19:21Z
date_updated: 2024-06-10T11:55:57Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco.2009.17.4.17403
intvolume: ' 17'
issue: '4'
language:
- iso: eng
page: 493-509
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Statistical Methods for Convergence Detection of Multi-Objective Evolutionary
Algorithms
type: journal_article
user_id: '15504'
volume: 17
year: '2009'
...