---
_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'
...
---
_id: '46407'
abstract:
- lang: eng
text: Choosing and tuning an optimization procedure for a given class of nonlinear
optimization problems is not an easy task. One way to proceed is to consider this
as a tournament, where each procedure will compete in different ‘disciplines’.
Here, disciplines could either be different functions, which we want to optimize,
or specific performance measures of the optimization procedure. We would then
be interested in the algorithm that performs best in a majority of cases or whose
average performance is maximal. We will focus on evolutionary multiobjective optimization
algorithms (EMOA), and will present a novel approach to the design and analysis
of evolutionary multiobjective benchmark experiments based on similar work from
the context of machine learning. We focus on deriving a consensus among several
benchmarks over different test problems and illustrate the methodology by reanalyzing
the results of the CEC 2007 EMOA competition.
author:
- 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: Boris
full_name: Naujoks, Boris
last_name: Naujoks
- first_name: Claus
full_name: Weihs, Claus
last_name: Weihs
citation:
ama: 'Mersmann O, Trautmann H, Naujoks B, Weihs C. Benchmarking evolutionary multiobjective
optimization algorithms. In: IEEE Congress on Evolutionary Computation.
; 2010:1-8. doi:10.1109/CEC.2010.5586241'
apa: Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). Benchmarking
evolutionary multiobjective optimization algorithms. IEEE Congress on Evolutionary
Computation, 1–8. https://doi.org/10.1109/CEC.2010.5586241
bibtex: '@inproceedings{Mersmann_Trautmann_Naujoks_Weihs_2010, title={Benchmarking
evolutionary multiobjective optimization algorithms}, DOI={10.1109/CEC.2010.5586241},
booktitle={IEEE Congress on Evolutionary Computation}, author={Mersmann, Olaf
and Trautmann, Heike and Naujoks, Boris and Weihs, Claus}, year={2010}, pages={1–8}
}'
chicago: Mersmann, Olaf, Heike Trautmann, Boris Naujoks, and Claus Weihs. “Benchmarking
Evolutionary Multiobjective Optimization Algorithms.” In IEEE Congress on Evolutionary
Computation, 1–8, 2010. https://doi.org/10.1109/CEC.2010.5586241.
ieee: 'O. Mersmann, H. Trautmann, B. Naujoks, and C. Weihs, “Benchmarking evolutionary
multiobjective optimization algorithms,” in IEEE Congress on Evolutionary Computation,
2010, pp. 1–8, doi: 10.1109/CEC.2010.5586241.'
mla: Mersmann, Olaf, et al. “Benchmarking Evolutionary Multiobjective Optimization
Algorithms.” IEEE Congress on Evolutionary Computation, 2010, pp. 1–8,
doi:10.1109/CEC.2010.5586241.
short: 'O. Mersmann, H. Trautmann, B. Naujoks, C. Weihs, in: IEEE Congress on Evolutionary
Computation, 2010, pp. 1–8.'
date_created: 2023-08-04T16:05:53Z
date_updated: 2023-10-16T13:56:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2010.5586241
language:
- iso: eng
page: 1-8
publication: IEEE Congress on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Benchmarking evolutionary multiobjective optimization algorithms
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46404'
author:
- first_name: J
full_name: Ding, J
last_name: Ding
- first_name: S
full_name: Wessing, S
last_name: Wessing
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: J
full_name: Mehnen, J
last_name: Mehnen
- first_name: B
full_name: Naujoks, B
last_name: Naujoks
citation:
ama: 'Ding J, Wessing S, Trautmann H, Mehnen J, Naujoks B. Sequential Parameter
Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing.
In: Teti R, ed. Proceedings of the 7$^th$ CIRP International Seminar on Intelligent
Computation in Manufacturing Engineering (CIRP ICME ’10). Copyright C.O.C.
Com. org. Conv.; 2010.'
apa: Ding, J., Wessing, S., Trautmann, H., Mehnen, J., & Naujoks, B. (2010).
Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation
of Additive Layer Manufacturing. In R. Teti (Ed.), Proceedings of the 7$^th$
CIRP International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’10). Copyright C.O.C. Com. org. Conv.
bibtex: '@inproceedings{Ding_Wessing_Trautmann_Mehnen_Naujoks_2010, place={Capri,
Italy}, title={Sequential Parameter Optimisation for Multi-Objective Evolutionary
Optimisation of Additive Layer Manufacturing}, booktitle={Proceedings of the 7$^th$
CIRP International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’10)}, publisher={Copyright C.O.C. Com. org. Conv.}, author={Ding,
J and Wessing, S and Trautmann, Heike and Mehnen, J and Naujoks, B}, editor={Teti,
R}, year={2010} }'
chicago: 'Ding, J, S Wessing, Heike Trautmann, J Mehnen, and B Naujoks. “Sequential
Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive
Layer Manufacturing.” In Proceedings of the 7$^th$ CIRP International Seminar
on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10), edited
by R Teti. Capri, Italy: Copyright C.O.C. Com. org. Conv., 2010.'
ieee: J. Ding, S. Wessing, H. Trautmann, J. Mehnen, and B. Naujoks, “Sequential
Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive
Layer Manufacturing,” in Proceedings of the 7$^th$ CIRP International Seminar
on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10), 2010.
mla: Ding, J., et al. “Sequential Parameter Optimisation for Multi-Objective Evolutionary
Optimisation of Additive Layer Manufacturing.” Proceedings of the 7$^th$ CIRP
International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’10), edited by R Teti, Copyright C.O.C. Com. org. Conv., 2010.
short: 'J. Ding, S. Wessing, H. Trautmann, J. Mehnen, B. Naujoks, in: R. Teti (Ed.),
Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation
in Manufacturing Engineering (CIRP ICME ’10), Copyright C.O.C. Com. org. Conv.,
Capri, Italy, 2010.'
date_created: 2023-08-04T16:01:38Z
date_updated: 2023-10-16T13:55:25Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Teti, R
last_name: Teti
language:
- iso: eng
place: Capri, Italy
publication: Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation
in Manufacturing Engineering (CIRP ICME ’10)
publisher: Copyright C.O.C. Com. org. Conv.
status: public
title: Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation
of Additive Layer Manufacturing
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46409'
abstract:
- lang: eng
text: Since many real-world optimization problems are noisy, vector optimization
algorithms that can cope with noise and uncertainty are required. We propose new,
robust selection strategies for evolutionary multi-objective optimization in the
presence of noise. We apply new measures of uncertainty for estimating the recently
introduced Pareto-dominance for uncertain and noisy environments (PDU). The first
measure is the inter-quartile range of the outcomes of repeated function evaluations.
The second is based on axis-aligned bounding boxes around the upper and lower
quantiles of the sampled fitness values in objective space. Experiments on real
and artificial problems show promising results.
author:
- first_name: Thomas
full_name: Voß, Thomas
last_name: Voß
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Igel, Christian
last_name: Igel
citation:
ama: 'Voß T, Trautmann H, Igel C. New Uncertainty Handling Strategies in Multi-objective
Evolutionary Optimization. In: Schaefer R, Cotta C, Kołodziej J, Rudolph G, eds.
Parallel Problem Solving from Nature, PPSN XI. Springer Berlin Heidelberg;
2010:260–269. doi:https://doi.org/10.1007/978-3-642-15871-1_27'
apa: Voß, T., Trautmann, H., & Igel, C. (2010). New Uncertainty Handling Strategies
in Multi-objective Evolutionary Optimization. In R. Schaefer, C. Cotta, J. Kołodziej,
& G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI
(pp. 260–269). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_27
bibtex: '@inproceedings{Voß_Trautmann_Igel_2010, place={Berlin, Heidelberg}, title={New
Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization},
DOI={https://doi.org/10.1007/978-3-642-15871-1_27},
booktitle={Parallel Problem Solving from Nature, PPSN XI}, publisher={Springer
Berlin Heidelberg}, author={Voß, Thomas and Trautmann, Heike and Igel, Christian},
editor={Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph,
Günter}, year={2010}, pages={260–269} }'
chicago: 'Voß, Thomas, Heike Trautmann, and Christian Igel. “New Uncertainty Handling
Strategies in Multi-Objective Evolutionary Optimization.” In Parallel Problem
Solving from Nature, PPSN XI, edited by Robert Schaefer, Carlos Cotta, Joanna
Kołodziej, and Günter Rudolph, 260–269. Berlin, Heidelberg: Springer Berlin Heidelberg,
2010. https://doi.org/10.1007/978-3-642-15871-1_27.'
ieee: 'T. Voß, H. Trautmann, and C. Igel, “New Uncertainty Handling Strategies in
Multi-objective Evolutionary Optimization,” in Parallel Problem Solving from
Nature, PPSN XI, 2010, pp. 260–269, doi: https://doi.org/10.1007/978-3-642-15871-1_27.'
mla: Voß, Thomas, et al. “New Uncertainty Handling Strategies in Multi-Objective
Evolutionary Optimization.” Parallel Problem Solving from Nature, PPSN XI,
edited by Robert Schaefer et al., Springer Berlin Heidelberg, 2010, pp. 260–269,
doi:https://doi.org/10.1007/978-3-642-15871-1_27.
short: 'T. Voß, H. Trautmann, C. Igel, in: R. Schaefer, C. Cotta, J. Kołodziej,
G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI, Springer Berlin
Heidelberg, Berlin, Heidelberg, 2010, pp. 260–269.'
date_created: 2023-08-04T16:07:48Z
date_updated: 2023-10-16T13:56:48Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1007/978-3-642-15871-1_27
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: 260–269
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: New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46412'
abstract:
- lang: eng
text: In this paper, a concept for efficiently approximating the practically relevant
regions of the Pareto front (PF) is introduced. Instead of the original objectives,
desirability functions (DFs) of the objectives are optimized, which express the
preferences of the decision maker. The original problem formulation and the optimization
algorithm do not have to be modified. DFs map an objective to the domain [0, 1]
and nonlinearly increase with better objective quality. By means of this mapping,
values of different objectives and units become comparable. A biased distribution
of the solutions in the PF approximation based on different scalings of the objectives
is prevented. Thus, we propose the integration of DFs into the S-metric selection
evolutionary multiobjective algorithm. The transformation ensures the meaning
of the hypervolumes internally computed. Furthermore, it is shown that the reference
point for the hypervolume calculation can be set intuitively. The approach is
analyzed using standard test problems. Moreover, a practical validation by means
of the optimization of a turning process is performed.
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
citation:
ama: Wagner T, Trautmann H. Integration of Preferences in Hypervolume-Based Multiobjective
Evolutionary Algorithms by Means of Desirability Functions. IEEE Transactions
on Evolutionary Computation. 2010;14(5):688-701. doi:10.1109/TEVC.2010.2058119
apa: Wagner, T., & Trautmann, H. (2010). Integration of Preferences in Hypervolume-Based
Multiobjective Evolutionary Algorithms by Means of Desirability Functions. IEEE
Transactions on Evolutionary Computation, 14(5), 688–701. https://doi.org/10.1109/TEVC.2010.2058119
bibtex: '@article{Wagner_Trautmann_2010, title={Integration of Preferences in Hypervolume-Based
Multiobjective Evolutionary Algorithms by Means of Desirability Functions}, volume={14},
DOI={10.1109/TEVC.2010.2058119},
number={5}, journal={IEEE Transactions on Evolutionary Computation}, author={Wagner,
Tobias and Trautmann, Heike}, year={2010}, pages={688–701} }'
chicago: 'Wagner, Tobias, and Heike Trautmann. “Integration of Preferences in Hypervolume-Based
Multiobjective Evolutionary Algorithms by Means of Desirability Functions.” IEEE
Transactions on Evolutionary Computation 14, no. 5 (2010): 688–701. https://doi.org/10.1109/TEVC.2010.2058119.'
ieee: 'T. Wagner and H. Trautmann, “Integration of Preferences in Hypervolume-Based
Multiobjective Evolutionary Algorithms by Means of Desirability Functions,” IEEE
Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 688–701, 2010,
doi: 10.1109/TEVC.2010.2058119.'
mla: Wagner, Tobias, and Heike Trautmann. “Integration of Preferences in Hypervolume-Based
Multiobjective Evolutionary Algorithms by Means of Desirability Functions.” IEEE
Transactions on Evolutionary Computation, vol. 14, no. 5, 2010, pp. 688–701,
doi:10.1109/TEVC.2010.2058119.
short: T. Wagner, H. Trautmann, IEEE Transactions on Evolutionary Computation 14
(2010) 688–701.
date_created: 2023-08-04T16:10:02Z
date_updated: 2023-10-16T13:57:41Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/TEVC.2010.2058119
intvolume: ' 14'
issue: '5'
language:
- iso: eng
page: 688-701
publication: IEEE Transactions on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary
Algorithms by Means of Desirability Functions
type: journal_article
user_id: '15504'
volume: 14
year: '2010'
...
---
_id: '46411'
abstract:
- lang: eng
text: The paper presents a framework to optimise the design of work roll based on
the cooling performance. The framework develops meta-models from a set of finite
element analyses (FEA) of the roll cooling. A design of experiment technique is
used to identify the FEA runs. The research also identifies sources of uncertainties
in the design process. A robust evolutionary multi-objective evaluation technique
is applied to the design optimisation in constrained problems with real life uncertainty.
The approach handles uncertainties associated both with design variables and fitness
functions. Constraints violation within the neighbourhood of a design is considered
as part of a measurement for degree of feasibility and robustness of a solution.
author:
- first_name: Y.T.
full_name: Azene, Y.T.
last_name: Azene
- first_name: R.
full_name: Roy, R.
last_name: Roy
- first_name: D.
full_name: Farrugia, D.
last_name: Farrugia
- first_name: C.
full_name: Onisa, C.
last_name: Onisa
- first_name: J.
full_name: Mehnen, J.
last_name: Mehnen
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Azene YT, Roy R, Farrugia D, Onisa C, Mehnen J, Trautmann H. Work roll cooling
system design optimisation in presence of uncertainty and constrains. CIRP
Journal of Manufacturing Science and Technology. 2010;2(4):290-298. doi:https://doi.org/10.1016/j.cirpj.2010.06.001
apa: Azene, Y. T., Roy, R., Farrugia, D., Onisa, C., Mehnen, J., & Trautmann,
H. (2010). Work roll cooling system design optimisation in presence of uncertainty
and constrains. CIRP Journal of Manufacturing Science and Technology, 2(4),
290–298. https://doi.org/10.1016/j.cirpj.2010.06.001
bibtex: '@article{Azene_Roy_Farrugia_Onisa_Mehnen_Trautmann_2010, title={Work roll
cooling system design optimisation in presence of uncertainty and constrains},
volume={2}, DOI={https://doi.org/10.1016/j.cirpj.2010.06.001},
number={4}, journal={CIRP Journal of Manufacturing Science and Technology}, author={Azene,
Y.T. and Roy, R. and Farrugia, D. and Onisa, C. and Mehnen, J. and Trautmann,
Heike}, year={2010}, pages={290–298} }'
chicago: 'Azene, Y.T., R. Roy, D. Farrugia, C. Onisa, J. Mehnen, and Heike Trautmann.
“Work Roll Cooling System Design Optimisation in Presence of Uncertainty and Constrains.”
CIRP Journal of Manufacturing Science and Technology 2, no. 4 (2010): 290–98.
https://doi.org/10.1016/j.cirpj.2010.06.001.'
ieee: 'Y. T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen, and H. Trautmann,
“Work roll cooling system design optimisation in presence of uncertainty and constrains,”
CIRP Journal of Manufacturing Science and Technology, vol. 2, no. 4, pp.
290–298, 2010, doi: https://doi.org/10.1016/j.cirpj.2010.06.001.'
mla: Azene, Y. T., et al. “Work Roll Cooling System Design Optimisation in Presence
of Uncertainty and Constrains.” CIRP Journal of Manufacturing Science and Technology,
vol. 2, no. 4, 2010, pp. 290–98, doi:https://doi.org/10.1016/j.cirpj.2010.06.001.
short: Y.T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen, H. Trautmann, CIRP
Journal of Manufacturing Science and Technology 2 (2010) 290–298.
date_created: 2023-08-04T16:09:19Z
date_updated: 2023-10-16T13:57:23Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.cirpj.2010.06.001
intvolume: ' 2'
issue: '4'
keyword:
- Roll cooling design
- Uncertainty
- Design optimisation
- Multi-objective optimisation
- Constraint in design
language:
- iso: eng
page: 290-298
publication: CIRP Journal of Manufacturing Science and Technology
publication_identifier:
issn:
- 1755-5817
status: public
title: Work roll cooling system design optimisation in presence of uncertainty and
constrains
type: journal_article
user_id: '15504'
volume: 2
year: '2010'
...
---
_id: '46410'
abstract:
- lang: eng
text: The design and application of termination criteria has become an important
aspect in evolutionary multi-objective optimization. Online convergence detection
(OCD) determines when further generations are no longer promising based on statistical
tests on a set of performance indicators. The behavior of OCD mainly depends on
two parameters, the number of preceding generations considered in the statistical
tests and the desired variance limit. In this paper, guidelines for selecting
appropriate combinations of these parameters are empirically derived based on
design-of-experiment methods. Furthermore, a variant of OCD is introduced which
directly operates on the hypervolume indicator - the internal measure of the SMS-EMOA.
This allows a separated analysis of the variance criterion and reduces the complexity
of OCD. Based on the experimental design, a systematic comparison with the classical
OCD approach is performed and differences between the appropriate parameterizations
of both variants are highlighted.
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
citation:
ama: 'Wagner T, Trautmann H. Online convergence detection for evolutionary multi-objective
algorithms revisited. In: IEEE Congress on Evolutionary Computation. ;
2010:1-8. doi:10.1109/CEC.2010.5586474'
apa: Wagner, T., & Trautmann, H. (2010). Online convergence detection for evolutionary
multi-objective algorithms revisited. IEEE Congress on Evolutionary Computation,
1–8. https://doi.org/10.1109/CEC.2010.5586474
bibtex: '@inproceedings{Wagner_Trautmann_2010, title={Online convergence detection
for evolutionary multi-objective algorithms revisited}, DOI={10.1109/CEC.2010.5586474},
booktitle={IEEE Congress on Evolutionary Computation}, author={Wagner, Tobias
and Trautmann, Heike}, year={2010}, pages={1–8} }'
chicago: Wagner, Tobias, and Heike Trautmann. “Online Convergence Detection for
Evolutionary Multi-Objective Algorithms Revisited.” In IEEE Congress on Evolutionary
Computation, 1–8, 2010. https://doi.org/10.1109/CEC.2010.5586474.
ieee: 'T. Wagner and H. Trautmann, “Online convergence detection for evolutionary
multi-objective algorithms revisited,” in IEEE Congress on Evolutionary Computation,
2010, pp. 1–8, doi: 10.1109/CEC.2010.5586474.'
mla: Wagner, Tobias, and Heike Trautmann. “Online Convergence Detection for Evolutionary
Multi-Objective Algorithms Revisited.” IEEE Congress on Evolutionary Computation,
2010, pp. 1–8, doi:10.1109/CEC.2010.5586474.
short: 'T. Wagner, H. Trautmann, in: IEEE Congress on Evolutionary Computation,
2010, pp. 1–8.'
date_created: 2023-08-04T16:08:41Z
date_updated: 2023-10-16T13:57:05Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2010.5586474
language:
- iso: eng
page: 1-8
publication: IEEE Congress on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Online convergence detection for evolutionary multi-objective algorithms revisited
type: conference
user_id: '15504'
year: '2010'
...
---
_id: '46414'
abstract:
- lang: eng
text: Over the last decades, evolutionary algorithms (EA) have proven their applicability
to hard and complex industrial optimization problems in many cases. However, especially
in cases with high computational demands for fitness evaluations (FE), the number
of required FE is often seen as a drawback of these techniques. This is partly
due to lacking robust and reliable methods to determine convergence, which would
stop the algorithm before useless evaluations are carried out. To overcome this
drawback, we define a method for online convergence detection (OCD) based on statistical
tests, which invokes a number of performance indicators and which can be applied
on a stand-alone basis (no predefined Pareto fronts, ideal and reference points).
Our experiments show the general applicability of OCD by analyzing its performance
for different algorithmic setups and on different classes of test functions. Furthermore,
we show that the number of FE can be reduced considerably – compared to common
suggestions from literature – without significantly deteriorating approximation
accuracy.
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: Boris
full_name: Naujoks, Boris
last_name: Naujoks
citation:
ama: 'Wagner T, Trautmann H, Naujoks B. OCD: Online Convergence Detection for Evolutionary
Multi-Objective Algorithms Based on Statistical Testing. In: Ehrgott M, Fonseca
CM, Gandibleux X, Hao J-K, Sevaux M, eds. Evolutionary Multi-Criterion Optimization.
Springer Berlin Heidelberg; 2009:198–215. doi:https://doi.org/10.1007/978-3-642-01020-0_19'
apa: 'Wagner, T., Trautmann, H., & Naujoks, B. (2009). OCD: Online Convergence
Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing.
In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, & M. Sevaux (Eds.),
Evolutionary Multi-Criterion Optimization (pp. 198–215). Springer Berlin
Heidelberg. https://doi.org/10.1007/978-3-642-01020-0_19'
bibtex: '@inproceedings{Wagner_Trautmann_Naujoks_2009, place={Berlin, Heidelberg},
title={OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms
Based on Statistical Testing}, DOI={https://doi.org/10.1007/978-3-642-01020-0_19},
booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Berlin
Heidelberg}, author={Wagner, Tobias and Trautmann, Heike and Naujoks, Boris},
editor={Ehrgott, Matthias and Fonseca, Carlos M. and Gandibleux, Xavier and Hao,
Jin-Kao and Sevaux, Marc}, year={2009}, pages={198–215} }'
chicago: 'Wagner, Tobias, Heike Trautmann, and Boris Naujoks. “OCD: Online Convergence
Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing.”
In Evolutionary Multi-Criterion Optimization, edited by Matthias Ehrgott,
Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, 198–215. Berlin,
Heidelberg: Springer Berlin Heidelberg, 2009. https://doi.org/10.1007/978-3-642-01020-0_19.'
ieee: 'T. Wagner, H. Trautmann, and B. Naujoks, “OCD: Online Convergence Detection
for Evolutionary Multi-Objective Algorithms Based on Statistical Testing,” in
Evolutionary Multi-Criterion Optimization, 2009, pp. 198–215, doi: https://doi.org/10.1007/978-3-642-01020-0_19.'
mla: 'Wagner, Tobias, et al. “OCD: Online Convergence Detection for Evolutionary
Multi-Objective Algorithms Based on Statistical Testing.” Evolutionary Multi-Criterion
Optimization, edited by Matthias Ehrgott et al., Springer Berlin Heidelberg,
2009, pp. 198–215, doi:https://doi.org/10.1007/978-3-642-01020-0_19.'
short: 'T. Wagner, H. Trautmann, B. Naujoks, in: M. Ehrgott, C.M. Fonseca, X. Gandibleux,
J.-K. Hao, M. Sevaux (Eds.), Evolutionary Multi-Criterion Optimization, Springer
Berlin Heidelberg, Berlin, Heidelberg, 2009, pp. 198–215.'
date_created: 2023-08-04T16:15:04Z
date_updated: 2023-10-16T13:58:13Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1007/978-3-642-01020-0_19
editor:
- first_name: Matthias
full_name: Ehrgott, Matthias
last_name: Ehrgott
- first_name: Carlos M.
full_name: Fonseca, Carlos M.
last_name: Fonseca
- first_name: Xavier
full_name: Gandibleux, Xavier
last_name: Gandibleux
- first_name: Jin-Kao
full_name: Hao, Jin-Kao
last_name: Hao
- first_name: Marc
full_name: Sevaux, Marc
last_name: Sevaux
language:
- iso: eng
page: 198–215
place: Berlin, Heidelberg
publication: Evolutionary Multi-Criterion Optimization
publication_identifier:
isbn:
- 978-3-642-01020-0
publisher: Springer Berlin Heidelberg
status: public
title: 'OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms
Based on Statistical Testing'
type: conference
user_id: '15504'
year: '2009'
...