---
_id: '46387'
abstract:
- lang: eng
text: Here we address the problem of computing finite size Hausdorff approximations
of the Pareto front of four-objective optimization problems by means of evolutionary
computing. Since many applications desire an approximation evenly spread along
the Pareto front and approximations that are good in the Hausdorff sense are typically
evenly spread along the Pareto front we consider three different evolutionary
multi-objective algorithms tailored to that purpose, where two of them are based
on the Part and Selection Algorithm (PSA). Finally, we present some numerical
results indicating the strength of the novel methods.
author:
- first_name: C
full_name: Dominguez-Medina, C
last_name: Dominguez-Medina
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Dominguez-Medina C, Rudolph G, Schütze O, Trautmann H. Evenly spaced Pareto
fronts of quad-objective problems using PSA partitioning technique. In: Proceedings
of the 2013 IEEE Congress on Evolutionary Computation (CEC). ; 2013:3190–3197.
doi:https://doi.org/10.1109/CEC.2013.6557960'
apa: Dominguez-Medina, C., Rudolph, G., Schütze, O., & Trautmann, H. (2013).
Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning
technique. Proceedings of the 2013 IEEE Congress on Evolutionary Computation
(CEC), 3190–3197. https://doi.org/10.1109/CEC.2013.6557960
bibtex: '@inproceedings{Dominguez-Medina_Rudolph_Schütze_Trautmann_2013, place={Cancun,
Mexico}, title={Evenly spaced Pareto fronts of quad-objective problems using PSA
partitioning technique}, DOI={https://doi.org/10.1109/CEC.2013.6557960},
booktitle={Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)},
author={Dominguez-Medina, C and Rudolph, G and Schütze, O and Trautmann, Heike},
year={2013}, pages={3190–3197} }'
chicago: Dominguez-Medina, C, G Rudolph, O Schütze, and Heike Trautmann. “Evenly
Spaced Pareto Fronts of Quad-Objective Problems Using PSA Partitioning Technique.”
In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC),
3190–3197. Cancun, Mexico, 2013. https://doi.org/10.1109/CEC.2013.6557960.
ieee: 'C. Dominguez-Medina, G. Rudolph, O. Schütze, and H. Trautmann, “Evenly spaced
Pareto fronts of quad-objective problems using PSA partitioning technique,” in
Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC),
2013, pp. 3190–3197, doi: https://doi.org/10.1109/CEC.2013.6557960.'
mla: Dominguez-Medina, C., et al. “Evenly Spaced Pareto Fronts of Quad-Objective
Problems Using PSA Partitioning Technique.” Proceedings of the 2013 IEEE Congress
on Evolutionary Computation (CEC), 2013, pp. 3190–3197, doi:https://doi.org/10.1109/CEC.2013.6557960.
short: 'C. Dominguez-Medina, G. Rudolph, O. Schütze, H. Trautmann, in: Proceedings
of the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 2013,
pp. 3190–3197.'
date_created: 2023-08-04T15:40:15Z
date_updated: 2023-10-16T13:45:34Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1109/CEC.2013.6557960
language:
- iso: eng
page: 3190–3197
place: Cancun, Mexico
publication: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)
status: public
title: Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning
technique
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '46389'
abstract:
- lang: eng
text: Current StarCraft bots are not very flexible in their strategy choice, most
of them just follow a manually optimized one, usually a rush. We suggest a method
of augmenting existing bots via Fuzzy Control in order to make them react on the
current game situation. According to the available information, the best matching
of a pool of strategies is chosen. While the method is very general and can be
applied easily to many bots, we implement it for the existing BTHAI bot and show
experimentally how the modifications affects its gameplay, and how it is improved
compared to the original version.
author:
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Daniel
full_name: Kozakowski, Daniel
last_name: Kozakowski
- first_name: Johan
full_name: Hagelbäck, Johan
last_name: Hagelbäck
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Preuss M, Kozakowski D, Hagelbäck J, Trautmann H. Reactive strategy choice
in StarCraft by means of Fuzzy Control. In: 2013 IEEE Conference on Computational
Inteligence in Games (CIG). ; 2013:1-8. doi:10.1109/CIG.2013.6633627'
apa: Preuss, M., Kozakowski, D., Hagelbäck, J., & Trautmann, H. (2013). Reactive
strategy choice in StarCraft by means of Fuzzy Control. 2013 IEEE Conference
on Computational Inteligence in Games (CIG), 1–8. https://doi.org/10.1109/CIG.2013.6633627
bibtex: '@inproceedings{Preuss_Kozakowski_Hagelbäck_Trautmann_2013, title={Reactive
strategy choice in StarCraft by means of Fuzzy Control}, DOI={10.1109/CIG.2013.6633627},
booktitle={2013 IEEE Conference on Computational Inteligence in Games (CIG)},
author={Preuss, Mike and Kozakowski, Daniel and Hagelbäck, Johan and Trautmann,
Heike}, year={2013}, pages={1–8} }'
chicago: Preuss, Mike, Daniel Kozakowski, Johan Hagelbäck, and Heike Trautmann.
“Reactive Strategy Choice in StarCraft by Means of Fuzzy Control.” In 2013
IEEE Conference on Computational Inteligence in Games (CIG), 1–8, 2013. https://doi.org/10.1109/CIG.2013.6633627.
ieee: 'M. Preuss, D. Kozakowski, J. Hagelbäck, and H. Trautmann, “Reactive strategy
choice in StarCraft by means of Fuzzy Control,” in 2013 IEEE Conference on
Computational Inteligence in Games (CIG), 2013, pp. 1–8, doi: 10.1109/CIG.2013.6633627.'
mla: Preuss, Mike, et al. “Reactive Strategy Choice in StarCraft by Means of Fuzzy
Control.” 2013 IEEE Conference on Computational Inteligence in Games (CIG),
2013, pp. 1–8, doi:10.1109/CIG.2013.6633627.
short: 'M. Preuss, D. Kozakowski, J. Hagelbäck, H. Trautmann, in: 2013 IEEE Conference
on Computational Inteligence in Games (CIG), 2013, pp. 1–8.'
date_created: 2023-08-04T15:42:58Z
date_updated: 2023-10-16T13:46:13Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CIG.2013.6633627
language:
- iso: eng
page: 1-8
publication: 2013 IEEE Conference on Computational Inteligence in Games (CIG)
status: public
title: Reactive strategy choice in StarCraft by means of Fuzzy Control
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '46395'
abstract:
- lang: eng
text: In multiobjective optimization, the identification of practically relevant
solutions on the Pareto-optimal front is an important research topic. Desirability
functions (DFs) allow the preferences of the decision maker to be specified in
an intuitive way. Recently, it has been shown for continuous optimization problems
that an a priori transformation of the objectives by means of DFs can be used
to focus the search of a hypervolume-based evolutionary algorithm on the desired
part of the front. In many-objective optimization, however, the computational
complexity of the hypervolume can become a crucial part. Thus, an alternative
to this approach will be presented in this paper. The new algorithm operates in
the untransformed objective space, but the desirability index (DI), that is, a
DF-based scalarization, will be used as the second-level selection criterion in
the non-dominated sorting. The diversity and uniform distribution of the resulting
approximation are ensured by the use of an external archive. In the experiments,
different preferences are specified as DFs, and their effects are investigated.
It is shown that trade-off solutions are generated in the desired regions of the
Pareto-optimal front and with a density adaptive to the DI. The efficiency of
the approach with respect to increasing objective space dimension is also analysed
using scalable test functions. The convergence speed is superior to other set-based
and preference-based evolutionary multiobjective algorithms while the approach
is of low computational complexity due to cheap DI evaluations. Copyright © 2013
John Wiley & Sons, Ltd.
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: D
full_name: Biermann, D
last_name: Biermann
- first_name: C
full_name: Weihs, C
last_name: Weihs
citation:
ama: Trautmann H, Wagner T, Biermann D, Weihs C. Indicator-based Selection in Evolutionary
Multiobjective Optimization Algorithms Based On the Desirability Index. Journal
of Multi-Criteria Decision Analysis. 2013;20(5-6):319–337. doi:https://doi.org/10.1002/mcda.1503
apa: Trautmann, H., Wagner, T., Biermann, D., & Weihs, C. (2013). Indicator-based
Selection in Evolutionary Multiobjective Optimization Algorithms Based On the
Desirability Index. Journal of Multi-Criteria Decision Analysis, 20(5–6),
319–337. https://doi.org/10.1002/mcda.1503
bibtex: '@article{Trautmann_Wagner_Biermann_Weihs_2013, title={Indicator-based Selection
in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability
Index}, volume={20}, DOI={https://doi.org/10.1002/mcda.1503},
number={5–6}, journal={Journal of Multi-Criteria Decision Analysis}, author={Trautmann,
Heike and Wagner, T and Biermann, D and Weihs, C}, year={2013}, pages={319–337}
}'
chicago: 'Trautmann, Heike, T Wagner, D Biermann, and C Weihs. “Indicator-Based
Selection in Evolutionary Multiobjective Optimization Algorithms Based On the
Desirability Index.” Journal of Multi-Criteria Decision Analysis 20, no.
5–6 (2013): 319–337. https://doi.org/10.1002/mcda.1503.'
ieee: 'H. Trautmann, T. Wagner, D. Biermann, and C. Weihs, “Indicator-based Selection
in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability
Index,” Journal of Multi-Criteria Decision Analysis, vol. 20, no. 5–6,
pp. 319–337, 2013, doi: https://doi.org/10.1002/mcda.1503.'
mla: Trautmann, Heike, et al. “Indicator-Based Selection in Evolutionary Multiobjective
Optimization Algorithms Based On the Desirability Index.” Journal of Multi-Criteria
Decision Analysis, vol. 20, no. 5–6, 2013, pp. 319–337, doi:https://doi.org/10.1002/mcda.1503.
short: H. Trautmann, T. Wagner, D. Biermann, C. Weihs, Journal of Multi-Criteria
Decision Analysis 20 (2013) 319–337.
date_created: 2023-08-04T15:50:03Z
date_updated: 2023-10-16T13:48:31Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1002/mcda.1503
intvolume: ' 20'
issue: 5-6
language:
- iso: eng
page: 319–337
publication: Journal of Multi-Criteria Decision Analysis
status: public
title: Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms
Based On the Desirability Index
type: journal_article
user_id: '15504'
volume: 20
year: '2013'
...
---
_id: '46393'
abstract:
- lang: eng
text: In multi-objective optimization, set-based performance indicators have become
the state of the art for assessing the quality of Pareto front approximations.
As a consequence, they are also more and more used within the design of multi-objective
optimization algorithms. The R2 and the Hypervolume (HV) indicator represent two
popular examples. In order to understand the behavior and the approximations preferred
by these indicators and algorithms, a comprehensive knowledge of the indicator’s
properties is required. Whereas this knowledge is available for the HV, we presented
a first approach in this direction for the R2 indicator just recently. In this
paper, we build upon this knowledge and enhance the considerations with respect
to the integration of preferences into the R2 indicator. More specifically, we
analyze the effect of the reference point, the domain of the weights, and the
distribution of weight vectors on the optimization of $\mu$ solutions with respect
to the R2 indicator. By means of theoretical findings and empirical evidence,
we show the potentials of these three possibilities using the optimal distribution
of $\mu$ solutions for exemplary setups.
author:
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Dimo
full_name: Brockhoff, Dimo
last_name: Brockhoff
citation:
ama: 'Wagner T, Trautmann H, Brockhoff D. Preference Articulation by Means of the
R2 Indicator. In: Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J, eds.
Evolutionary Multi-Criterion Optimization. Springer Berlin Heidelberg;
2013:81–95.'
apa: Wagner, T., Trautmann, H., & Brockhoff, D. (2013). Preference Articulation
by Means of the R2 Indicator. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca,
S. Greco, & J. Shaw (Eds.), Evolutionary Multi-Criterion Optimization
(pp. 81–95). Springer Berlin Heidelberg.
bibtex: '@inproceedings{Wagner_Trautmann_Brockhoff_2013, place={Berlin, Heidelberg},
title={Preference Articulation by Means of the R2 Indicator}, booktitle={Evolutionary
Multi-Criterion Optimization}, publisher={Springer Berlin Heidelberg}, author={Wagner,
Tobias and Trautmann, Heike and Brockhoff, Dimo}, editor={Purshouse, Robin C.
and Fleming, Peter J. and Fonseca, Carlos M. and Greco, Salvatore and Shaw, Jane},
year={2013}, pages={81–95} }'
chicago: 'Wagner, Tobias, Heike Trautmann, and Dimo Brockhoff. “Preference Articulation
by Means of the R2 Indicator.” In Evolutionary Multi-Criterion Optimization,
edited by Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco,
and Jane Shaw, 81–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.'
ieee: T. Wagner, H. Trautmann, and D. Brockhoff, “Preference Articulation by Means
of the R2 Indicator,” in Evolutionary Multi-Criterion Optimization, 2013,
pp. 81–95.
mla: Wagner, Tobias, et al. “Preference Articulation by Means of the R2 Indicator.”
Evolutionary Multi-Criterion Optimization, edited by Robin C. Purshouse
et al., Springer Berlin Heidelberg, 2013, pp. 81–95.
short: 'T. Wagner, H. Trautmann, D. Brockhoff, in: R.C. Purshouse, P.J. Fleming,
C.M. Fonseca, S. Greco, J. Shaw (Eds.), Evolutionary Multi-Criterion Optimization,
Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 81–95.'
date_created: 2023-08-04T15:47:49Z
date_updated: 2023-10-16T13:47:58Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Robin C.
full_name: Purshouse, Robin C.
last_name: Purshouse
- first_name: Peter J.
full_name: Fleming, Peter J.
last_name: Fleming
- first_name: Carlos M.
full_name: Fonseca, Carlos M.
last_name: Fonseca
- first_name: Salvatore
full_name: Greco, Salvatore
last_name: Greco
- first_name: Jane
full_name: Shaw, Jane
last_name: Shaw
language:
- iso: eng
page: 81–95
place: Berlin, Heidelberg
publication: Evolutionary Multi-Criterion Optimization
publication_identifier:
isbn:
- 978-3-642-37140-0
publisher: Springer Berlin Heidelberg
status: public
title: Preference Articulation by Means of the R2 Indicator
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '46392'
abstract:
- lang: eng
text: An indicator-based evolutionary multiobjective optimization algorithm (EMOA)
is introduced which incorporates the contribution to the unary R2-indicator as
the secondary selection criterion. First experiments indicate that the R2-EMOA
accurately approximates the Pareto front of the considered continuous multiobjective
optimization problems. Furthermore, decision makers’ preferences can be included
by adjusting the weight vector distributions of the indicator which results in
a focused search behavior.
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Dimo
full_name: Brockhoff, Dimo
last_name: Brockhoff
citation:
ama: 'Trautmann H, Wagner T, Brockhoff D. R2-EMOA: Focused Multiobjective Search
Using R2-Indicator-Based Selection. In: Nicosia G, Pardalos P, eds. Learning
and Intelligent Optimization. Springer Berlin Heidelberg; 2013:70–74.'
apa: 'Trautmann, H., Wagner, T., & Brockhoff, D. (2013). R2-EMOA: Focused Multiobjective
Search Using R2-Indicator-Based Selection. In G. Nicosia & P. Pardalos (Eds.),
Learning and Intelligent Optimization (pp. 70–74). Springer Berlin Heidelberg.'
bibtex: '@inproceedings{Trautmann_Wagner_Brockhoff_2013, place={Berlin, Heidelberg},
title={R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection},
booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin
Heidelberg}, author={Trautmann, Heike and Wagner, Tobias and Brockhoff, Dimo},
editor={Nicosia, Giuseppe and Pardalos, Panos}, year={2013}, pages={70–74} }'
chicago: 'Trautmann, Heike, Tobias Wagner, and Dimo Brockhoff. “R2-EMOA: Focused
Multiobjective Search Using R2-Indicator-Based Selection.” In Learning and
Intelligent Optimization, edited by Giuseppe Nicosia and Panos Pardalos, 70–74.
Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.'
ieee: 'H. Trautmann, T. Wagner, and D. Brockhoff, “R2-EMOA: Focused Multiobjective
Search Using R2-Indicator-Based Selection,” in Learning and Intelligent Optimization,
2013, pp. 70–74.'
mla: 'Trautmann, Heike, et al. “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based
Selection.” Learning and Intelligent Optimization, edited by Giuseppe Nicosia
and Panos Pardalos, Springer Berlin Heidelberg, 2013, pp. 70–74.'
short: 'H. Trautmann, T. Wagner, D. Brockhoff, in: G. Nicosia, P. Pardalos (Eds.),
Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg,
2013, pp. 70–74.'
date_created: 2023-08-04T15:47:00Z
date_updated: 2023-10-16T13:47:41Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Giuseppe
full_name: Nicosia, Giuseppe
last_name: Nicosia
- first_name: Panos
full_name: Pardalos, Panos
last_name: Pardalos
language:
- iso: eng
page: 70–74
place: Berlin, Heidelberg
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-642-44973-4
publisher: Springer Berlin Heidelberg
status: public
title: 'R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection'
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '48889'
abstract:
- lang: eng
text: Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization
problems. With this paper we contribute to the understanding of the success of
2-opt based local search algorithms for solving the traveling salesperson problem
(TSP). Although 2-opt is widely used in practice, it is hard to understand its
success from a theoretical perspective. We take a statistical approach and examine
the features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- 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: Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F. A Novel Feature-Based
Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem.
Annals of Mathematics and Artificial Intelligence. 2013;69(2):151–182.
doi:10.1007/s10472-013-9341-2
apa: Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann,
F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesperson Problem. Annals of Mathematics and Artificial
Intelligence, 69(2), 151–182. https://doi.org/10.1007/s10472-013-9341-2
bibtex: '@article{Mersmann_Bischl_Trautmann_Wagner_Bossek_Neumann_2013, title={A
Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling
Salesperson Problem}, volume={69}, DOI={10.1007/s10472-013-9341-2},
number={2}, journal={Annals of Mathematics and Artificial Intelligence}, author={Mersmann,
Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob
and Neumann, Frank}, year={2013}, pages={151–182} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek,
and Frank Neumann. “A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesperson Problem.” Annals of Mathematics and Artificial
Intelligence 69, no. 2 (2013): 151–182. https://doi.org/10.1007/s10472-013-9341-2.'
ieee: 'O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann,
“A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesperson Problem,” Annals of Mathematics and Artificial Intelligence,
vol. 69, no. 2, pp. 151–182, 2013, doi: 10.1007/s10472-013-9341-2.'
mla: Mersmann, Olaf, et al. “A Novel Feature-Based Approach to Characterize Algorithm
Performance for the Traveling Salesperson Problem.” Annals of Mathematics and
Artificial Intelligence, vol. 69, no. 2, 2013, pp. 151–182, doi:10.1007/s10472-013-9341-2.
short: O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals
of Mathematics and Artificial Intelligence 69 (2013) 151–182.
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:50:41Z
department:
- _id: '819'
doi: 10.1007/s10472-013-9341-2
intvolume: ' 69'
issue: '2'
keyword:
- 2-opt
- 90B06
- Classification
- Feature selection
- MARS
- TSP
language:
- iso: eng
page: 151–182
publication: Annals of Mathematics and Artificial Intelligence
publication_identifier:
issn:
- 1012-2443
status: public
title: A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesperson Problem
type: journal_article
user_id: '102979'
volume: 69
year: '2013'
...
---
_id: '46394'
abstract:
- lang: eng
text: Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization
problems. With this paper we contribute to the understanding of the success of
2-opt based local search algorithms for solving the traveling salesperson problem
(TSP). Although 2-opt is widely used in practice, it is hard to understand its
success from a theoretical perspective. We take a statistical approach and examine
the features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: O
full_name: Mersmann, O
last_name: Mersmann
- first_name: B
full_name: Bischl, B
last_name: Bischl
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: M
full_name: Wagner, M
last_name: Wagner
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: F
full_name: Neumann, F
last_name: Neumann
citation:
ama: Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F. A Novel Feature-Based
Approach to Characterize Algorithm Performance for the Traveling Salesman Problem.
Annals of Mathematics and Artificial Intelligence. 2013;69:151–182.
apa: Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann,
F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence,
69, 151–182.
bibtex: '@article{Mersmann_Bischl_Trautmann_Wagner_Bossek_Neumann_2013, title={A
Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling
Salesman Problem}, volume={69}, journal={Annals of Mathematics and Artificial
Intelligence}, author={Mersmann, O and Bischl, B and Trautmann, Heike and Wagner,
M and Bossek, Jakob and Neumann, F}, year={2013}, pages={151–182} }'
chicago: 'Mersmann, O, B Bischl, Heike Trautmann, M Wagner, Jakob Bossek, and F
Neumann. “A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesman Problem.” Annals of Mathematics and Artificial Intelligence
69 (2013): 151–182.'
ieee: O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann,
“A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesman Problem,” Annals of Mathematics and Artificial Intelligence,
vol. 69, pp. 151–182, 2013.
mla: Mersmann, O., et al. “A Novel Feature-Based Approach to Characterize Algorithm
Performance for the Traveling Salesman Problem.” Annals of Mathematics and
Artificial Intelligence, vol. 69, 2013, pp. 151–182.
short: O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals
of Mathematics and Artificial Intelligence 69 (2013) 151–182.
date_created: 2023-08-04T15:48:57Z
date_updated: 2024-06-10T11:57:43Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 69'
language:
- iso: eng
page: 151–182
publication: Annals of Mathematics and Artificial Intelligence
status: public
title: A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesman Problem
type: journal_article
user_id: '15504'
volume: 69
year: '2013'
...
---
_id: '46397'
abstract:
- lang: eng
text: In multiobjective optimization, set-based performance indicators are commonly
used to assess the quality of a Pareto front approximation. Based on the scalarization
obtained by these indicators, a performance comparison of multiobjective optimization
algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent
two recommended approaches which have shown a correlated behavior in recent empirical
studies. Whereas the HV indicator has been comprehensively analyzed in the last
years, almost no studies on the R2 indicator exist. In this paper, we thus perform
a comprehensive investigation of the properties of the R2 indicator in a theoretical
and empirical way. The influence of the number and distribution of the weight
vectors on the optimal distribution of μ solutions is analyzed. Based on a comparative
analysis, specific characteristics and differences of the R2 and HV indicator
are presented.
author:
- first_name: Dimo
full_name: Brockhoff, Dimo
last_name: Brockhoff
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Brockhoff D, Wagner T, Trautmann H. On the Properties of the R2 Indicator.
In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation.
GECCO ’12. Association for Computing Machinery; 2012:465–472. doi:10.1145/2330163.2330230'
apa: Brockhoff, D., Wagner, T., & Trautmann, H. (2012). On the Properties of
the R2 Indicator. Proceedings of the 14th Annual Conference on Genetic and
Evolutionary Computation, 465–472. https://doi.org/10.1145/2330163.2330230
bibtex: '@inproceedings{Brockhoff_Wagner_Trautmann_2012, place={New York, NY, USA},
series={GECCO ’12}, title={On the Properties of the R2 Indicator}, DOI={10.1145/2330163.2330230},
booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation}, publisher={Association for Computing Machinery}, author={Brockhoff,
Dimo and Wagner, Tobias and Trautmann, Heike}, year={2012}, pages={465–472}, collection={GECCO
’12} }'
chicago: 'Brockhoff, Dimo, Tobias Wagner, and Heike Trautmann. “On the Properties
of the R2 Indicator.” In Proceedings of the 14th Annual Conference on Genetic
and Evolutionary Computation, 465–472. GECCO ’12. New York, NY, USA: Association
for Computing Machinery, 2012. https://doi.org/10.1145/2330163.2330230.'
ieee: 'D. Brockhoff, T. Wagner, and H. Trautmann, “On the Properties of the R2 Indicator,”
in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
2012, pp. 465–472, doi: 10.1145/2330163.2330230.'
mla: Brockhoff, Dimo, et al. “On the Properties of the R2 Indicator.” Proceedings
of the 14th Annual Conference on Genetic and Evolutionary Computation, Association
for Computing Machinery, 2012, pp. 465–472, doi:10.1145/2330163.2330230.
short: 'D. Brockhoff, T. Wagner, H. Trautmann, in: Proceedings of the 14th Annual
Conference on Genetic and Evolutionary Computation, Association for Computing
Machinery, New York, NY, USA, 2012, pp. 465–472.'
date_created: 2023-08-04T15:52:42Z
date_updated: 2023-10-16T13:47:23Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2330163.2330230
keyword:
- hypervolume indicator
- multiobjective optimization
- performance assessment
- r2 indicator
language:
- iso: eng
page: 465–472
place: New York, NY, USA
publication: Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- '9781450311779'
publisher: Association for Computing Machinery
series_title: GECCO ’12
status: public
title: On the Properties of the R2 Indicator
type: conference
user_id: '15504'
year: '2012'
...
---
_id: '46396'
abstract:
- lang: eng
text: The steady supply of new optimization methods makes the algorithm selection
problem (ASP) an increasingly pressing and challenging task, specially for real-world
black-box optimization problems. The introduced approach considers the ASP as
a cost-sensitive classification task which is based on Exploratory Landscape Analysis.
Low-level features gathered by systematic sampling of the function on the feasible
set are used to predict a well-performing algorithm out of a given portfolio.
Example-specific label costs are defined by the expected runtime of each candidate
algorithm. We use one-sided support vector regression to solve this learning problem.
The approach is illustrated by means of the optimization problems and algorithms
of the BBOB’09/10 workshop.
author:
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
citation:
ama: 'Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory
Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association
for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209'
apa: Bischl, B., Mersmann, O., Trautmann, H., & Preuß, M. (2012). Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.
Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. https://doi.org/10.1145/2330163.2330209
bibtex: '@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY,
USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape
Analysis and Cost-Sensitive Learning}, DOI={10.1145/2330163.2330209},
booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation}, publisher={Association for Computing Machinery}, author={Bischl,
Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320},
collection={GECCO ’12} }'
chicago: 'Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”
In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012.
https://doi.org/10.1145/2330163.2330209.'
ieee: 'B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection
Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings
of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012,
pp. 313–320, doi: 10.1145/2330163.2330209.'
mla: Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis
and Cost-Sensitive Learning.” Proceedings of the 14th Annual Conference on
Genetic and Evolutionary Computation, Association for Computing Machinery,
2012, pp. 313–320, doi:10.1145/2330163.2330209.
short: 'B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation, Association for Computing
Machinery, New York, NY, USA, 2012, pp. 313–320.'
date_created: 2023-08-04T15:51:56Z
date_updated: 2023-10-16T13:48:48Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2330163.2330209
keyword:
- machine learning
- exploratory landscape analysis
- fitness landscape
- benchmarking
- evolutionary optimization
- bbob test set
- algorithm selection
language:
- iso: eng
page: 313–320
place: New York, NY, USA
publication: Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- '9781450311779'
publisher: Association for Computing Machinery
series_title: GECCO ’12
status: public
title: Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive
Learning
type: conference
user_id: '15504'
year: '2012'
...
---
_id: '46399'
abstract:
- lang: eng
text: Meta-modeling has become a crucial tool in solving expensive optimization
problems. Much of the work in the past has focused on finding a good regression
method to model the fitness function. Examples include classical linear regression,
splines, neural networks, Kriging and support vector regression. This paper specifically
draws attention to the fact that assessing model accuracy is a crucial aspect
in the meta-modeling framework. Resampling strategies such as cross-validation,
subsampling, bootstrapping, and nested resampling are prominent methods for model
validation and are systematically discussed with respect to possible pitfalls,
shortcomings, and specific features. A survey of meta-modeling techniques within
evolutionary optimization is provided. In addition, practical examples illustrating
some of the pitfalls associated with model selection and performance assessment
are presented. Finally, recommendations are given for choosing a model validation
technique for a particular setting.
author:
- first_name: B
full_name: Bischl, B
last_name: Bischl
- first_name: O
full_name: Mersmann, O
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: C
full_name: Weihs, C
last_name: Weihs
citation:
ama: Bischl B, Mersmann O, Trautmann H, Weihs C. Resampling Methods in Model Validation.
Evolutionary Computation Journal. 2012;20(2):249–275. doi:10.1162/EVCO_a_00069
apa: Bischl, B., Mersmann, O., Trautmann, H., & Weihs, C. (2012). Resampling
Methods in Model Validation. Evolutionary Computation Journal, 20(2),
249–275. https://doi.org/10.1162/EVCO_a_00069
bibtex: '@article{Bischl_Mersmann_Trautmann_Weihs_2012, title={Resampling Methods
in Model Validation}, volume={20}, DOI={10.1162/EVCO_a_00069},
number={2}, journal={Evolutionary Computation Journal}, author={Bischl, B and
Mersmann, O and Trautmann, Heike and Weihs, C}, year={2012}, pages={249–275} }'
chicago: 'Bischl, B, O Mersmann, Heike Trautmann, and C Weihs. “Resampling Methods
in Model Validation.” Evolutionary Computation Journal 20, no. 2 (2012):
249–275. https://doi.org/10.1162/EVCO_a_00069.'
ieee: 'B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs, “Resampling Methods in
Model Validation,” Evolutionary Computation Journal, vol. 20, no. 2, pp.
249–275, 2012, doi: 10.1162/EVCO_a_00069.'
mla: Bischl, B., et al. “Resampling Methods in Model Validation.” Evolutionary
Computation Journal, vol. 20, no. 2, 2012, pp. 249–275, doi:10.1162/EVCO_a_00069.
short: B. Bischl, O. Mersmann, H. Trautmann, C. Weihs, Evolutionary Computation
Journal 20 (2012) 249–275.
date_created: 2023-08-04T15:54:41Z
date_updated: 2023-10-16T13:53:58Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/EVCO_a_00069
intvolume: ' 20'
issue: '2'
language:
- iso: eng
page: 249–275
publication: Evolutionary Computation Journal
status: public
title: Resampling Methods in Model Validation
type: journal_article
user_id: '15504'
volume: 20
year: '2012'
...
---
_id: '46400'
abstract:
- lang: ger
text: Es werden mehrkriterielle evolutio-näre Algorithmen (EMOA) für zwei- und höherdimensio-nale Probleme vorgestellt, die gleichmäßig verteilte Lö-sungen entlang der wahren Paretofront generieren. Diesist insbesondere wichtig im Kontext mehrkriterieller Kon-trollprobleme. Die Methodik beruht auf der Minimierungdes gemittelten Hausdorff-Abstandes
in Bezug aufdie Paretofront. Die EMOA-Varianten werden vergleichendzu aktuellen Verfahren auf Benchmarkproblemen getestet.
author:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: O
full_name: Schütze, O
last_name: Schütze
citation:
ama: Rudolph G, Trautmann H, Schütze O. Homogene Approximation der Paretofront bei
mehrkriteriellen Kontrollproblemen. at-Automatisierungstechnik. 2012;60:610–621.
doi:10.1524/auto.2012.1033
apa: Rudolph, G., Trautmann, H., & Schütze, O. (2012). Homogene Approximation
der Paretofront bei mehrkriteriellen Kontrollproblemen. At-Automatisierungstechnik,
60, 610–621. https://doi.org/10.1524/auto.2012.1033
bibtex: '@article{Rudolph_Trautmann_Schütze_2012, title={Homogene Approximation
der Paretofront bei mehrkriteriellen Kontrollproblemen}, volume={60}, DOI={10.1524/auto.2012.1033}, journal={at-Automatisierungstechnik},
author={Rudolph, G and Trautmann, Heike and Schütze, O}, year={2012}, pages={610–621}
}'
chicago: 'Rudolph, G, Heike Trautmann, and O Schütze. “Homogene Approximation Der
Paretofront Bei Mehrkriteriellen Kontrollproblemen.” At-Automatisierungstechnik
60 (2012): 610–621. https://doi.org/10.1524/auto.2012.1033.'
ieee: 'G. Rudolph, H. Trautmann, and O. Schütze, “Homogene Approximation der Paretofront
bei mehrkriteriellen Kontrollproblemen,” at-Automatisierungstechnik, vol.
60, pp. 610–621, 2012, doi: 10.1524/auto.2012.1033.'
mla: Rudolph, G., et al. “Homogene Approximation Der Paretofront Bei Mehrkriteriellen
Kontrollproblemen.” At-Automatisierungstechnik, vol. 60, 2012, pp. 610–621,
doi:10.1524/auto.2012.1033.
short: G. Rudolph, H. Trautmann, O. Schütze, At-Automatisierungstechnik 60 (2012)
610–621.
date_created: 2023-08-04T15:55:34Z
date_updated: 2023-10-16T13:54:17Z
department:
- _id: '34'
- _id: '819'
doi: 10.1524/auto.2012.1033
intvolume: ' 60'
language:
- iso: eng
page: 610–621
publication: at-Automatisierungstechnik
status: public
title: Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen
type: journal_article
user_id: '15504'
volume: 60
year: '2012'
...
---
_id: '48890'
abstract:
- lang: eng
text: With this paper we contribute to the understanding of the success of 2-opt
based local search algorithms for solving the traveling salesman problem TSP.
Although 2-opt is widely used in practice, it is hard to understand its success
from a theoretical perspective. We take a statistical approach and examine the
features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- 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
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search
and the Traveling Salesman Problem: A Feature-Based Characterization of Problem
Hardness. In: Revised Selected Papers of the 6th International Conference on
Learning and Intelligent Optimization - Volume 7219. LION 6. Springer-Verlag;
2012:115–129.'
apa: 'Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann,
F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness. Revised Selected Papers of the 6th International Conference
on Learning and Intelligent Optimization - Volume 7219, 115–129.'
bibtex: '@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin,
Heidelberg}, series={LION 6}, title={Local Search and the Traveling Salesman Problem:
A Feature-Based Characterization of Problem Hardness}, booktitle={Revised Selected
Papers of the 6th International Conference on Learning and Intelligent Optimization
- Volume 7219}, publisher={Springer-Verlag}, author={Mersmann, Olaf and Bischl,
Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank},
year={2012}, pages={115–129}, collection={LION 6} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner,
and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness.” In Revised Selected Papers of the 6th
International Conference on Learning and Intelligent Optimization - Volume 7219,
115–129. LION 6. Berlin, Heidelberg: Springer-Verlag, 2012.'
ieee: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann,
“Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness,” in Revised Selected Papers of the 6th International Conference
on Learning and Intelligent Optimization - Volume 7219, 2012, pp. 115–129.'
mla: 'Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A
Feature-Based Characterization of Problem Hardness.” Revised Selected Papers
of the 6th International Conference on Learning and Intelligent Optimization -
Volume 7219, Springer-Verlag, 2012, pp. 115–129.'
short: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann,
in: Revised Selected Papers of the 6th International Conference on Learning and
Intelligent Optimization - Volume 7219, Springer-Verlag, Berlin, Heidelberg, 2012,
pp. 115–129.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:58Z
department:
- _id: '819'
extern: '1'
keyword:
- 2-opt
- Classification
- Feature Selection
- MARS
- TSP
language:
- iso: eng
page: 115–129
place: Berlin, Heidelberg
publication: Revised Selected Papers of the 6th International Conference on Learning
and Intelligent Optimization - Volume 7219
publication_identifier:
isbn:
- 978-3-642-34412-1
publisher: Springer-Verlag
series_title: LION 6
status: public
title: 'Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness'
type: conference
user_id: '102979'
year: '2012'
...
---
_id: '48888'
abstract:
- lang: eng
text: With this paper we contribute to the understanding of the success of 2-opt
based local search algorithms for solving the traveling salesman problem (TSP).
Although 2-opt is widely used in practice, it is hard to understand its success
from a theoretical perspective. We take a statistical approach and examine the
features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- 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
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search
and the Traveling Salesman Problem: A Feature-Based Characterization of Problem
Hardness. In: Learning and Intelligent Optimization. Vol 7219. Springer
Berlin Heidelberg; 2012:115–129. doi:10.1007/978-3-642-34413-8_9'
apa: 'Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann,
F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness. In Learning and Intelligent Optimization (Vol. 7219,
pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9'
bibtex: '@inbook{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin,
Heidelberg}, title={Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness}, volume={7219}, DOI={10.1007/978-3-642-34413-8_9},
booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin
Heidelberg}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann,
Heike and Wagner, Markus and Neumann, Frank}, year={2012}, pages={115–129} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner,
and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness.” In Learning and Intelligent Optimization,
7219:115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-34413-8_9.'
ieee: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann,
“Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness,” in Learning and Intelligent Optimization, vol. 7219,
Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 115–129.'
mla: 'Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A
Feature-Based Characterization of Problem Hardness.” Learning and Intelligent
Optimization, vol. 7219, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:10.1007/978-3-642-34413-8_9.'
short: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann,
in: Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin,
Heidelberg, 2012, pp. 115–129.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:15Z
department:
- _id: '819'
doi: 10.1007/978-3-642-34413-8_9
extern: '1'
intvolume: ' 7219'
language:
- iso: eng
page: 115–129
place: Berlin, Heidelberg
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-642-34412-1 978-3-642-34413-8
publisher: Springer Berlin Heidelberg
status: public
title: 'Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness'
type: book_chapter
user_id: '102979'
volume: 7219
year: '2012'
...
---
_id: '46398'
abstract:
- lang: eng
text: With this paper we contribute to the understanding of the success of 2-opt
based local search algorithms for solving the traveling salesman problem (TSP).
Although 2-opt is widely used in practice, it is hard to understand its success
from a theoretical perspective. We take a statistical approach and examine the
features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- 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: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search
and the Traveling Salesman Problem: A Feature-Based Characterization of Problem
Hardness. In: Hamadi Y, Schoenauer M, eds. Learning and Intelligent Optimization.
Springer Berlin Heidelberg; 2012:115–129. doi:https://doi.org/10.1007/978-3-642-34413-8_9'
apa: 'Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann,
F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness. In Y. Hamadi & M. Schoenauer (Eds.), Learning and
Intelligent Optimization (pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9'
bibtex: '@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin,
Heidelberg}, title={Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness}, DOI={https://doi.org/10.1007/978-3-642-34413-8_9},
booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin
Heidelberg}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann,
Heike and Wagner, Markus and Neumann, Frank}, editor={Hamadi, Youssef and Schoenauer,
Marc}, year={2012}, pages={115–129} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner,
and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness.” In Learning and Intelligent Optimization,
edited by Youssef Hamadi and Marc Schoenauer, 115–129. Berlin, Heidelberg: Springer
Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-34413-8_9.'
ieee: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann,
“Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness,” in Learning and Intelligent Optimization, 2012, pp.
115–129, doi: https://doi.org/10.1007/978-3-642-34413-8_9.'
mla: 'Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A
Feature-Based Characterization of Problem Hardness.” Learning and Intelligent
Optimization, edited by Youssef Hamadi and Marc Schoenauer, Springer Berlin
Heidelberg, 2012, pp. 115–129, doi:https://doi.org/10.1007/978-3-642-34413-8_9.'
short: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann,
in: Y. Hamadi, M. Schoenauer (Eds.), Learning and Intelligent Optimization, Springer
Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 115–129.'
date_created: 2023-08-04T15:53:33Z
date_updated: 2024-06-10T11:57:32Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1007/978-3-642-34413-8_9
editor:
- first_name: Youssef
full_name: Hamadi, Youssef
last_name: Hamadi
- first_name: Marc
full_name: Schoenauer, Marc
last_name: Schoenauer
language:
- iso: eng
page: 115–129
place: Berlin, Heidelberg
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-642-34413-8
publisher: Springer Berlin Heidelberg
status: public
title: 'Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness'
type: conference
user_id: '15504'
year: '2012'
...
---
_id: '46401'
abstract:
- lang: eng
text: Exploratory Landscape Analysis subsumes a number of techniques employed to
obtain knowledge about the properties of an unknown optimization problem, especially
insofar as these properties are important for the performance of optimization
algorithms. Where in a first attempt, one could rely on high-level features designed
by experts, we approach the problem from a different angle here, namely by using
relatively cheap low-level computer generated features. Interestingly, very few
features are needed to separate the BBOB problem groups and also for relating
a problem to high-level, expert designed features, paving the way for automatic
algorithm selection.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Claus
full_name: Weihs, Claus
last_name: Weihs
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
citation:
ama: 'Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G. Exploratory
Landscape Analysis. In: Proceedings of the 13th Annual Conference on Genetic
and Evolutionary Computation. GECCO ’11. Association for Computing Machinery;
2011:829–836. doi:10.1145/2001576.2001690'
apa: Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph,
G. (2011). Exploratory Landscape Analysis. Proceedings of the 13th Annual Conference
on Genetic and Evolutionary Computation, 829–836. https://doi.org/10.1145/2001576.2001690
bibtex: '@inproceedings{Mersmann_Bischl_Trautmann_Preuss_Weihs_Rudolph_2011, place={New
York, NY, USA}, series={GECCO ’11}, title={Exploratory Landscape Analysis}, DOI={10.1145/2001576.2001690}, booktitle={Proceedings
of the 13th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association
for Computing Machinery}, author={Mersmann, Olaf and Bischl, Bernd and Trautmann,
Heike and Preuss, Mike and Weihs, Claus and Rudolph, Günter}, year={2011}, pages={829–836},
collection={GECCO ’11} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs,
and Günter Rudolph. “Exploratory Landscape Analysis.” In Proceedings of the
13th Annual Conference on Genetic and Evolutionary Computation, 829–836. GECCO
’11. New York, NY, USA: Association for Computing Machinery, 2011. https://doi.org/10.1145/2001576.2001690.'
ieee: 'O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph,
“Exploratory Landscape Analysis,” in Proceedings of the 13th Annual Conference
on Genetic and Evolutionary Computation, 2011, pp. 829–836, doi: 10.1145/2001576.2001690.'
mla: Mersmann, Olaf, et al. “Exploratory Landscape Analysis.” Proceedings of
the 13th Annual Conference on Genetic and Evolutionary Computation, Association
for Computing Machinery, 2011, pp. 829–836, doi:10.1145/2001576.2001690.
short: 'O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, G. Rudolph, in:
Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation,
Association for Computing Machinery, New York, NY, USA, 2011, pp. 829–836.'
date_created: 2023-08-04T15:58:22Z
date_updated: 2023-10-16T13:54:34Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2001576.2001690
keyword:
- exploratory landscape analysis
- evolutionary optimization
- fitness landscape
- benchmarking
- BBOB test set
language:
- iso: eng
page: 829–836
place: New York, NY, USA
publication: Proceedings of the 13th Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- '9781450305570'
publisher: Association for Computing Machinery
series_title: GECCO ’11
status: public
title: Exploratory Landscape Analysis
type: conference
user_id: '15504'
year: '2011'
...
---
_id: '46402'
abstract:
- lang: eng
text: The use of multi-objective evolutionary algorithms for solving black-box problems
with multiple conflicting objectives has become an important research area. However,
when no gradient information is available, the examination of formal convergence
or optimality criteria is often impossible. Thus, sophisticated heuristic online
stopping criteria (OSC) have recently become subject of intensive research. In
order to establish formal guidelines for a systematic research, we present a taxonomy
of OSC in this paper. We integrate the known approaches within the taxonomy and
discuss them by extracting their building blocks. The formal structure of the
taxonomy is used as a basis for the implementation of a comprehensive MATLAB toolbox.
Both contributions, the formal taxonomy and the MATLAB implementation, provide
a framework for the analysis and evaluation of existing and new OSC approaches.
author:
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Luis
full_name: Martí, Luis
last_name: Martí
citation:
ama: 'Wagner T, Trautmann H, Martí L. A Taxonomy of Online Stopping Criteria for
Multi-Objective Evolutionary Algorithms. In: Takahashi RHC, Deb K, Wanner EF,
Greco S, eds. Evolutionary Multi-Criterion Optimization. Springer Berlin
Heidelberg; 2011:16–30. doi:https://doi.org/10.1007/978-3-642-19893-9_2'
apa: Wagner, T., Trautmann, H., & Martí, L. (2011). A Taxonomy of Online Stopping
Criteria for Multi-Objective Evolutionary Algorithms. In R. H. C. Takahashi, K.
Deb, E. F. Wanner, & S. Greco (Eds.), Evolutionary Multi-Criterion Optimization
(pp. 16–30). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_2
bibtex: '@inproceedings{Wagner_Trautmann_Martí_2011, place={Berlin, Heidelberg},
title={A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary
Algorithms}, DOI={https://doi.org/10.1007/978-3-642-19893-9_2},
booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Berlin
Heidelberg}, author={Wagner, Tobias and Trautmann, Heike and Martí, Luis}, editor={Takahashi,
Ricardo H. C. and Deb, Kalyanmoy and Wanner, Elizabeth F. and Greco, Salvatore},
year={2011}, pages={16–30} }'
chicago: 'Wagner, Tobias, Heike Trautmann, and Luis Martí. “A Taxonomy of Online
Stopping Criteria for Multi-Objective Evolutionary Algorithms.” In Evolutionary
Multi-Criterion Optimization, edited by Ricardo H. C. Takahashi, Kalyanmoy
Deb, Elizabeth F. Wanner, and Salvatore Greco, 16–30. Berlin, Heidelberg: Springer
Berlin Heidelberg, 2011. https://doi.org/10.1007/978-3-642-19893-9_2.'
ieee: 'T. Wagner, H. Trautmann, and L. Martí, “A Taxonomy of Online Stopping Criteria
for Multi-Objective Evolutionary Algorithms,” in Evolutionary Multi-Criterion
Optimization, 2011, pp. 16–30, doi: https://doi.org/10.1007/978-3-642-19893-9_2.'
mla: Wagner, Tobias, et al. “A Taxonomy of Online Stopping Criteria for Multi-Objective
Evolutionary Algorithms.” Evolutionary Multi-Criterion Optimization, edited
by Ricardo H. C. Takahashi et al., Springer Berlin Heidelberg, 2011, pp. 16–30,
doi:https://doi.org/10.1007/978-3-642-19893-9_2.
short: 'T. Wagner, H. Trautmann, L. Martí, in: R.H.C. Takahashi, K. Deb, E.F. Wanner,
S. Greco (Eds.), Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg,
Berlin, Heidelberg, 2011, pp. 16–30.'
date_created: 2023-08-04T15:59:18Z
date_updated: 2023-10-16T13:54:50Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1007/978-3-642-19893-9_2
editor:
- first_name: Ricardo H. C.
full_name: Takahashi, Ricardo H. C.
last_name: Takahashi
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Elizabeth F.
full_name: Wanner, Elizabeth F.
last_name: Wanner
- first_name: Salvatore
full_name: Greco, Salvatore
last_name: Greco
language:
- iso: eng
page: 16–30
place: Berlin, Heidelberg
publication: Evolutionary Multi-Criterion Optimization
publication_identifier:
isbn:
- 978-3-642-19893-9
publisher: Springer Berlin Heidelberg
status: public
title: A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms
type: conference
user_id: '15504'
year: '2011'
...
---
_id: '46403'
abstract:
- lang: eng
text: ' Evolutionary (multi-objective optimization) algorithms (EMOAs) are widely
accepted to be competitive optimization methods in industry today. However, normally
only standard techniques are employed by the engineering experts. Here, it is
shown how these standard techniques can be completed and improved with respect
to interactivity to other tools, runtime, and parameterization. The coupling with
metamodels serves as an example for the interactivity to other tools, while the
online convergence detection relates to runtime, i.e. stopping criteria. Finally,
sequential parameter optimization improves results focussing on parameter tuning.
We show that invoking all these methods on their own already enhances EMOAs for
aerodynamic applications. It is concluded with an outlook on how these methods
might come together to foster aerospace applications and, at a time, widen the
application area to multi-disciplinary optimization tasks. '
author:
- first_name: B
full_name: Naujoks, B
last_name: Naujoks
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: S
full_name: Wessing, S
last_name: Wessing
- first_name: C
full_name: Weihs, C
last_name: Weihs
citation:
ama: 'Naujoks B, Trautmann H, Wessing S, Weihs C. Advanced concepts for multi-objective
evolutionary optimization in aircraft industry. Proceedings of the Institution
of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2011;225(10):1081-1096.
doi:10.1177/0954410011414120'
apa: 'Naujoks, B., Trautmann, H., Wessing, S., & Weihs, C. (2011). Advanced
concepts for multi-objective evolutionary optimization in aircraft industry. Proceedings
of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,
225(10), 1081–1096. https://doi.org/10.1177/0954410011414120'
bibtex: '@article{Naujoks_Trautmann_Wessing_Weihs_2011, title={Advanced concepts
for multi-objective evolutionary optimization in aircraft industry}, volume={225},
DOI={10.1177/0954410011414120},
number={10}, journal={Proceedings of the Institution of Mechanical Engineers,
Part G: Journal of Aerospace Engineering}, author={Naujoks, B and Trautmann, Heike
and Wessing, S and Weihs, C}, year={2011}, pages={1081–1096} }'
chicago: 'Naujoks, B, Heike Trautmann, S Wessing, and C Weihs. “Advanced Concepts
for Multi-Objective Evolutionary Optimization in Aircraft Industry.” Proceedings
of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
225, no. 10 (2011): 1081–96. https://doi.org/10.1177/0954410011414120.'
ieee: 'B. Naujoks, H. Trautmann, S. Wessing, and C. Weihs, “Advanced concepts for
multi-objective evolutionary optimization in aircraft industry,” Proceedings
of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,
vol. 225, no. 10, pp. 1081–1096, 2011, doi: 10.1177/0954410011414120.'
mla: 'Naujoks, B., et al. “Advanced Concepts for Multi-Objective Evolutionary Optimization
in Aircraft Industry.” Proceedings of the Institution of Mechanical Engineers,
Part G: Journal of Aerospace Engineering, vol. 225, no. 10, 2011, pp. 1081–96,
doi:10.1177/0954410011414120.'
short: 'B. Naujoks, H. Trautmann, S. Wessing, C. Weihs, Proceedings of the Institution
of Mechanical Engineers, Part G: Journal of Aerospace Engineering 225 (2011) 1081–1096.'
date_created: 2023-08-04T16:00:28Z
date_updated: 2023-10-16T13:55:07Z
department:
- _id: '34'
- _id: '819'
doi: 10.1177/0954410011414120
intvolume: ' 225'
issue: '10'
language:
- iso: eng
page: 1081-1096
publication: 'Proceedings of the Institution of Mechanical Engineers, Part G: Journal
of Aerospace Engineering'
status: public
title: Advanced concepts for multi-objective evolutionary optimization in aircraft
industry
type: journal_article
user_id: '15504'
volume: 225
year: '2011'
...
---
_id: '46408'
abstract:
- lang: eng
text: The integration of experts’ preferences is an important aspect in multi-objective
optimization. Usually, one out of a set of Pareto optimal solutions has to be
chosen based on expert knowledge. A combination of multi-objective particle swarm
optimization (MOPSO) with the desirability concept is introduced to efficiently
focus on desired and relevant regions of the true Pareto front of the optimization
problem which facilitates the solution selection process. Desirability functions
of the objectives are optimized, and the desirability index is used for selecting
the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO
in most cases exclusively generates solutions in the desired area of the Pareto
front. Approximations of the whole Pareto front result in cases of misspecified
desired regions.
author:
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- 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: 'Mostaghim S, Trautmann H, Mersmann O. Preference-Based Multi-Objective Particle
Swarm Optimization Using Desirabilities. In: Schaefer R, Cotta C, Kołodziej J,
Rudolph G, eds. Parallel Problem Solving from Nature, PPSN XI. Springer
Berlin Heidelberg; 2010:101–110. doi:https://doi.org/10.1007/978-3-642-15871-1_11'
apa: Mostaghim, S., Trautmann, H., & Mersmann, O. (2010). Preference-Based Multi-Objective
Particle Swarm Optimization Using Desirabilities. In R. Schaefer, C. Cotta, J.
Kołodziej, & G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN
XI (pp. 101–110). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_11
bibtex: '@inproceedings{Mostaghim_Trautmann_Mersmann_2010, place={Berlin, Heidelberg},
title={Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities},
DOI={https://doi.org/10.1007/978-3-642-15871-1_11},
booktitle={Parallel Problem Solving from Nature, PPSN XI}, publisher={Springer
Berlin Heidelberg}, author={Mostaghim, Sanaz and Trautmann, Heike and Mersmann,
Olaf}, editor={Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph,
Günter}, year={2010}, pages={101–110} }'
chicago: 'Mostaghim, Sanaz, Heike Trautmann, and Olaf Mersmann. “Preference-Based
Multi-Objective Particle Swarm Optimization Using Desirabilities.” In Parallel
Problem Solving from Nature, PPSN XI, edited by Robert Schaefer, Carlos Cotta,
Joanna Kołodziej, and Günter Rudolph, 101–110. Berlin, Heidelberg: Springer Berlin
Heidelberg, 2010. https://doi.org/10.1007/978-3-642-15871-1_11.'
ieee: 'S. Mostaghim, H. Trautmann, and O. Mersmann, “Preference-Based Multi-Objective
Particle Swarm Optimization Using Desirabilities,” in Parallel Problem Solving
from Nature, PPSN XI, 2010, pp. 101–110, doi: https://doi.org/10.1007/978-3-642-15871-1_11.'
mla: Mostaghim, Sanaz, et al. “Preference-Based Multi-Objective Particle Swarm Optimization
Using Desirabilities.” Parallel Problem Solving from Nature, PPSN XI, edited
by Robert Schaefer et al., Springer Berlin Heidelberg, 2010, pp. 101–110, doi:https://doi.org/10.1007/978-3-642-15871-1_11.
short: 'S. Mostaghim, H. Trautmann, O. Mersmann, in: R. Schaefer, C. Cotta, J. Kołodziej,
G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI, Springer Berlin
Heidelberg, Berlin, Heidelberg, 2010, pp. 101–110.'
date_created: 2023-08-04T16:06:43Z
date_updated: 2023-10-16T13:56:31Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1007/978-3-642-15871-1_11
editor:
- first_name: Robert
full_name: Schaefer, Robert
last_name: Schaefer
- first_name: Carlos
full_name: Cotta, Carlos
last_name: Cotta
- first_name: Joanna
full_name: Kołodziej, Joanna
last_name: Kołodziej
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
language:
- iso: eng
page: 101–110
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature, PPSN XI
publication_identifier:
isbn:
- 978-3-642-15871-1
publisher: Springer Berlin Heidelberg
status: public
title: Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46405'
abstract:
- lang: eng
text: 'We present methods to answer two basic questions that arise when benchmarking
optimization algorithms. The first one is: which algorithm is the ’best’ one?
and the second one: which algorithm should I use for my real world problem? Both
are connected and neither is easy to answer. We present methods which can be used
to analyse the raw data of a benchmark experiment and derive some insight regarding
the answers to these questions. We employ the presented methods to analyse the
BBOB’09 benchmark results and present some initial findings.'
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Mersmann O, Preuss M, Trautmann H. Benchmarking Evolutionary Algorithms: Towards
Exploratory Landscape Analysis. In: Proceedings of the 11th International Conference
on Parallel Problem Solving from Nature: Part I. PPSN’10. Springer-Verlag;
2010:73–82.'
apa: 'Mersmann, O., Preuss, M., & Trautmann, H. (2010). Benchmarking Evolutionary
Algorithms: Towards Exploratory Landscape Analysis. Proceedings of the 11th
International Conference on Parallel Problem Solving from Nature: Part I,
73–82.'
bibtex: '@inproceedings{Mersmann_Preuss_Trautmann_2010, place={Berlin, Heidelberg},
series={PPSN’10}, title={Benchmarking Evolutionary Algorithms: Towards Exploratory
Landscape Analysis}, booktitle={Proceedings of the 11th International Conference
on Parallel Problem Solving from Nature: Part I}, publisher={Springer-Verlag},
author={Mersmann, Olaf and Preuss, Mike and Trautmann, Heike}, year={2010}, pages={73–82},
collection={PPSN’10} }'
chicago: 'Mersmann, Olaf, Mike Preuss, and Heike Trautmann. “Benchmarking Evolutionary
Algorithms: Towards Exploratory Landscape Analysis.” In Proceedings of the
11th International Conference on Parallel Problem Solving from Nature: Part I,
73–82. PPSN’10. Berlin, Heidelberg: Springer-Verlag, 2010.'
ieee: 'O. Mersmann, M. Preuss, and H. Trautmann, “Benchmarking Evolutionary Algorithms:
Towards Exploratory Landscape Analysis,” in Proceedings of the 11th International
Conference on Parallel Problem Solving from Nature: Part I, 2010, pp. 73–82.'
mla: 'Mersmann, Olaf, et al. “Benchmarking Evolutionary Algorithms: Towards Exploratory
Landscape Analysis.” Proceedings of the 11th International Conference on Parallel
Problem Solving from Nature: Part I, Springer-Verlag, 2010, pp. 73–82.'
short: 'O. Mersmann, M. Preuss, H. Trautmann, in: Proceedings of the 11th International
Conference on Parallel Problem Solving from Nature: Part I, Springer-Verlag, Berlin,
Heidelberg, 2010, pp. 73–82.'
date_created: 2023-08-04T16:02:28Z
date_updated: 2023-10-16T13:55:43Z
department:
- _id: '34'
- _id: '819'
keyword:
- benchmarking
- multidimensional scaling
- consensus ranking
- evolutionary optimization
- BBOB test set
language:
- iso: eng
page: 73–82
place: Berlin, Heidelberg
publication: 'Proceedings of the 11th International Conference on Parallel Problem
Solving from Nature: Part I'
publication_identifier:
isbn:
- '3642158439'
publisher: Springer-Verlag
series_title: PPSN’10
status: public
title: 'Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis'
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46406'
abstract:
- lang: eng
text: 'We present methods to answer two basic questions that arise when benchmarking
optimization algorithms. The first one is: which algorithm is the ''best'' one?
and the second one: which algorithm should I use for my real world problem? Both
are connected and neither is easy to answer. We present methods which can be used
to analyse the raw data of a benchmark experiment and derive some insight regarding
the answers to these questions. We employ the presented methods to analyse the
BBOB''09 benchmark results and present some initial findings.'
author:
- first_name: O
full_name: Mersmann, O
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: B
full_name: Naujoks, B
last_name: Naujoks
- first_name: C
full_name: Weihs, C
last_name: Weihs
citation:
ama: 'Mersmann O, Trautmann H, Naujoks B, Weihs C. On the Distribution of EMOA Hypervolumes.
In: Blum C, Battiti R, eds. Learning and Intelligent Optimization, 4$^th$ International
Conference, LION 4, Venice, Italy. Vol 6073. Lecture Notes in Computer Science.
Springer; 2010:333–337.'
apa: Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). On the Distribution
of EMOA Hypervolumes. In C. Blum & R. Battiti (Eds.), Learning and Intelligent
Optimization, 4$^th$ International Conference, LION 4, Venice, Italy (Vol.
6073, pp. 333–337). Springer.
bibtex: '@inproceedings{Mersmann_Trautmann_Naujoks_Weihs_2010, series={Lecture Notes
in Computer Science}, title={On the Distribution of EMOA Hypervolumes}, volume={6073},
booktitle={Learning and Intelligent Optimization, 4$^th$ International Conference,
LION 4, Venice, Italy}, publisher={Springer}, author={Mersmann, O and Trautmann,
Heike and Naujoks, B and Weihs, C}, editor={Blum, C and Battiti, R}, year={2010},
pages={333–337}, collection={Lecture Notes in Computer Science} }'
chicago: Mersmann, O, Heike Trautmann, B Naujoks, and C Weihs. “On the Distribution
of EMOA Hypervolumes.” In Learning and Intelligent Optimization, 4$^th$ International
Conference, LION 4, Venice, Italy, edited by C Blum and R Battiti, 6073:333–337.
Lecture Notes in Computer Science. Springer, 2010.
ieee: O. Mersmann, H. Trautmann, B. Naujoks, and C. Weihs, “On the Distribution
of EMOA Hypervolumes,” in Learning and Intelligent Optimization, 4$^th$ International
Conference, LION 4, Venice, Italy, 2010, vol. 6073, pp. 333–337.
mla: Mersmann, O., et al. “On the Distribution of EMOA Hypervolumes.” Learning
and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice,
Italy, edited by C Blum and R Battiti, vol. 6073, Springer, 2010, pp. 333–337.
short: 'O. Mersmann, H. Trautmann, B. Naujoks, C. Weihs, in: C. Blum, R. Battiti
(Eds.), Learning and Intelligent Optimization, 4$^th$ International Conference,
LION 4, Venice, Italy, Springer, 2010, pp. 333–337.'
date_created: 2023-08-04T16:03:45Z
date_updated: 2023-10-16T13:55:59Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: C
full_name: Blum, C
last_name: Blum
- first_name: R
full_name: Battiti, R
last_name: Battiti
intvolume: ' 6073'
language:
- iso: eng
page: 333–337
publication: Learning and Intelligent Optimization, 4$^th$ International Conference,
LION 4, Venice, Italy
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: On the Distribution of EMOA Hypervolumes
type: conference
user_id: '15504'
volume: 6073
year: '2010'
...