---
_id: '46415'
abstract:
- lang: eng
text: Noisy environments are a challenging task for multiobjective evolutionary
algorithms. The algorithms may be trapped in local optima or even become a random
search in the decision and objective space. In the course of the paper the classical
definition of Pareto-dominance is enhanced subject to noisy objective functions
in order to make the evolutionary search process more robust and to generate a
reliable Pareto front. At each point in the decision space the objective functions
are evaluated a fixed number of times and the convex hull of the objective function
vectors is computed. Expectation is associated with the median of the objective
function values while uncertainty is reflected by the average distance of the
median in each dimension to the points defining the convex hull. By combining
these two indicators a new concept of Pareto-dominance is set up. An implementation
in NSGA-II and application to test problems show a gain in robustness and search
quality.
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jorn
full_name: Mehnen, Jorn
last_name: Mehnen
- first_name: Boris
full_name: Naujoks, Boris
last_name: Naujoks
citation:
ama: 'Trautmann H, Mehnen J, Naujoks B. Pareto-dominance in noisy environments.
In: 2009 IEEE Congress on Evolutionary Computation. ; 2009:3119-3126. doi:10.1109/CEC.2009.4983338'
apa: Trautmann, H., Mehnen, J., & Naujoks, B. (2009). Pareto-dominance in noisy
environments. 2009 IEEE Congress on Evolutionary Computation, 3119–3126.
https://doi.org/10.1109/CEC.2009.4983338
bibtex: '@inproceedings{Trautmann_Mehnen_Naujoks_2009, title={Pareto-dominance in
noisy environments}, DOI={10.1109/CEC.2009.4983338},
booktitle={2009 IEEE Congress on Evolutionary Computation}, author={Trautmann,
Heike and Mehnen, Jorn and Naujoks, Boris}, year={2009}, pages={3119–3126} }'
chicago: Trautmann, Heike, Jorn Mehnen, and Boris Naujoks. “Pareto-Dominance in
Noisy Environments.” In 2009 IEEE Congress on Evolutionary Computation,
3119–26, 2009. https://doi.org/10.1109/CEC.2009.4983338.
ieee: 'H. Trautmann, J. Mehnen, and B. Naujoks, “Pareto-dominance in noisy environments,”
in 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 3119–3126,
doi: 10.1109/CEC.2009.4983338.'
mla: Trautmann, Heike, et al. “Pareto-Dominance in Noisy Environments.” 2009
IEEE Congress on Evolutionary Computation, 2009, pp. 3119–26, doi:10.1109/CEC.2009.4983338.
short: 'H. Trautmann, J. Mehnen, B. Naujoks, in: 2009 IEEE Congress on Evolutionary
Computation, 2009, pp. 3119–3126.'
date_created: 2023-08-04T16:16:32Z
date_updated: 2023-10-16T13:58:36Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2009.4983338
language:
- iso: eng
page: 3119-3126
publication: 2009 IEEE Congress on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Pareto-dominance in noisy environments
type: conference
user_id: '15504'
year: '2009'
...
---
_id: '46413'
abstract:
- lang: eng
text: Industry applications of multiobjective optimization problems mostly are characterized
by the demand for high quality solutions on the one hand. On the other hand an
optimization result is desired which at any rate meets the time constraints for
the evolutionary multiobjective algorithms (EMOA). The handling of this trade-off
is a frequently discussed issue in multiobjective evolutionary optimization.
author:
- first_name: Boris
full_name: Naujoks, Boris
last_name: Naujoks
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Naujoks B, Trautmann H. Online convergence detection for multiobjective aerodynamic
applications. In: 2009 IEEE Congress on Evolutionary Computation. ; 2009:332-339.
doi:10.1109/CEC.2009.4982966'
apa: Naujoks, B., & Trautmann, H. (2009). Online convergence detection for multiobjective
aerodynamic applications. 2009 IEEE Congress on Evolutionary Computation,
332–339. https://doi.org/10.1109/CEC.2009.4982966
bibtex: '@inproceedings{Naujoks_Trautmann_2009, title={Online convergence detection
for multiobjective aerodynamic applications}, DOI={10.1109/CEC.2009.4982966},
booktitle={2009 IEEE Congress on Evolutionary Computation}, author={Naujoks, Boris
and Trautmann, Heike}, year={2009}, pages={332–339} }'
chicago: Naujoks, Boris, and Heike Trautmann. “Online Convergence Detection for
Multiobjective Aerodynamic Applications.” In 2009 IEEE Congress on Evolutionary
Computation, 332–39, 2009. https://doi.org/10.1109/CEC.2009.4982966.
ieee: 'B. Naujoks and H. Trautmann, “Online convergence detection for multiobjective
aerodynamic applications,” in 2009 IEEE Congress on Evolutionary Computation,
2009, pp. 332–339, doi: 10.1109/CEC.2009.4982966.'
mla: Naujoks, Boris, and Heike Trautmann. “Online Convergence Detection for Multiobjective
Aerodynamic Applications.” 2009 IEEE Congress on Evolutionary Computation,
2009, pp. 332–39, doi:10.1109/CEC.2009.4982966.
short: 'B. Naujoks, H. Trautmann, in: 2009 IEEE Congress on Evolutionary Computation,
2009, pp. 332–339.'
date_created: 2023-08-04T16:14:08Z
date_updated: 2023-10-16T13:57:58Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2009.4982966
language:
- iso: eng
page: 332-339
publication: 2009 IEEE Congress on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Online convergence detection for multiobjective aerodynamic applications
type: conference
user_id: '15504'
year: '2009'
...
---
_id: '46416'
author:
- 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
citation:
ama: Trautmann H, Mehnen J. Statistical Methods for Improving Multi-objective Evolutionary
Optimisation. International Journal of Computational Intelligence Research.
2009;5(2):72–78.
apa: Trautmann, H., & Mehnen, J. (2009). Statistical Methods for Improving Multi-objective
Evolutionary Optimisation. International Journal of Computational Intelligence
Research, 5(2), 72–78.
bibtex: '@article{Trautmann_Mehnen_2009, title={Statistical Methods for Improving
Multi-objective Evolutionary Optimisation}, volume={5}, number={2}, journal={International
Journal of Computational Intelligence Research}, author={Trautmann, Heike and
Mehnen, J}, year={2009}, pages={72–78} }'
chicago: 'Trautmann, Heike, and J Mehnen. “Statistical Methods for Improving Multi-Objective
Evolutionary Optimisation.” International Journal of Computational Intelligence
Research 5, no. 2 (2009): 72–78.'
ieee: H. Trautmann and J. Mehnen, “Statistical Methods for Improving Multi-objective
Evolutionary Optimisation,” International Journal of Computational Intelligence
Research, vol. 5, no. 2, pp. 72–78, 2009.
mla: Trautmann, Heike, and J. Mehnen. “Statistical Methods for Improving Multi-Objective
Evolutionary Optimisation.” International Journal of Computational Intelligence
Research, vol. 5, no. 2, 2009, pp. 72–78.
short: H. Trautmann, J. Mehnen, International Journal of Computational Intelligence
Research 5 (2009) 72–78.
date_created: 2023-08-04T16:17:19Z
date_updated: 2023-10-16T13:58:54Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 5'
issue: '2'
language:
- iso: eng
page: 72–78
publication: International Journal of Computational Intelligence Research
status: public
title: Statistical Methods for Improving Multi-objective Evolutionary Optimisation
type: journal_article
user_id: '15504'
volume: 5
year: '2009'
...
---
_id: '46417'
abstract:
- lang: eng
text: ' In this article a method for including a priori preferences of decision
makers into multicriteria optimization problems is presented. A set of Pareto-optimal
solutions is determined via desirability functions of the objectives which reveal
experts’ preferences regarding different objective regions. An application to
noisy objective functions is not straightforward but very relevant for practical
applications. Two approaches are introduced in order to handle the respective
uncertainties by means of the proposed preference-based Pareto optimization. By
applying the methods to the original and uncertain Binh problem and a noisy single
cut turning cost optimization problem, these approaches prove to be very effective
in focusing on different parts of the Pareto front of the ori-ginal problem in
both certain and noisy environments. '
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jörn
full_name: Mehnen, Jörn
last_name: Mehnen
citation:
ama: Trautmann H, Mehnen J. Preference-based Pareto optimization in certain and
noisy environments. Engineering Optimization. 2009;41(1):23-38. doi:10.1080/03052150802347926
apa: Trautmann, H., & Mehnen, J. (2009). Preference-based Pareto optimization
in certain and noisy environments. Engineering Optimization, 41(1),
23–38. https://doi.org/10.1080/03052150802347926
bibtex: '@article{Trautmann_Mehnen_2009, title={Preference-based Pareto optimization
in certain and noisy environments}, volume={41}, DOI={10.1080/03052150802347926},
number={1}, journal={Engineering Optimization}, publisher={Taylor & Francis},
author={Trautmann, Heike and Mehnen, Jörn}, year={2009}, pages={23–38} }'
chicago: 'Trautmann, Heike, and Jörn Mehnen. “Preference-Based Pareto Optimization
in Certain and Noisy Environments.” Engineering Optimization 41, no. 1
(2009): 23–38. https://doi.org/10.1080/03052150802347926.'
ieee: 'H. Trautmann and J. Mehnen, “Preference-based Pareto optimization in certain
and noisy environments,” Engineering Optimization, vol. 41, no. 1, pp.
23–38, 2009, doi: 10.1080/03052150802347926.'
mla: Trautmann, Heike, and Jörn Mehnen. “Preference-Based Pareto Optimization in
Certain and Noisy Environments.” Engineering Optimization, vol. 41, no.
1, Taylor & Francis, 2009, pp. 23–38, doi:10.1080/03052150802347926.
short: H. Trautmann, J. Mehnen, Engineering Optimization 41 (2009) 23–38.
date_created: 2023-08-04T16:18:31Z
date_updated: 2023-10-16T13:59:31Z
department:
- _id: '34'
- _id: '819'
doi: 10.1080/03052150802347926
intvolume: ' 41'
issue: '1'
language:
- iso: eng
page: 23-38
publication: Engineering Optimization
publisher: Taylor & Francis
status: public
title: Preference-based Pareto optimization in certain and noisy environments
type: journal_article
user_id: '15504'
volume: 41
year: '2009'
...
---
_id: '46418'
abstract:
- lang: eng
text: In this paper, two approaches for estimating the generation in which a multi-objective
evolutionary algorithm (MOEA) shows statistically significant signs of convergence
are introduced. A set-based perspective is taken where convergence is measured
by performance indicators. The proposed techniques fulfill the requirements of
proper statistical assessment on the one hand and efficient optimisation for real-world
problems on the other hand. The first approach accounts for the stochastic nature
of the MOEA by repeating the optimisation runs for increasing generation numbers
and analysing the performance indicators using statistical tools. This technique
results in a very robust offline procedure. Moreover, an online convergence detection
method is introduced as well. This method automatically stops the MOEA when either
the variance of the performance indicators falls below a specified threshold or
a stagnation of their overall trend is detected. Both methods are analysed and
compared for two MOEA and on different classes of benchmark functions. It is shown
that the methods successfully operate on all stated problems needing less function
evaluations while preserving good approximation quality at the same time.
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: T.
full_name: Wagner, T.
last_name: Wagner
- first_name: B.
full_name: Naujoks, B.
last_name: Naujoks
- first_name: M.
full_name: Preuss, M.
last_name: Preuss
- first_name: J.
full_name: Mehnen, J.
last_name: Mehnen
citation:
ama: Trautmann H, Wagner T, Naujoks B, Preuss M, Mehnen J. Statistical Methods for
Convergence Detection of Multi-Objective Evolutionary Algorithms. Evolutionary
Computation. 2009;17(4):493-509. doi:10.1162/evco.2009.17.4.17403
apa: Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., & Mehnen, J. (2009).
Statistical Methods for Convergence Detection of Multi-Objective Evolutionary
Algorithms. Evolutionary Computation, 17(4), 493–509. https://doi.org/10.1162/evco.2009.17.4.17403
bibtex: '@article{Trautmann_Wagner_Naujoks_Preuss_Mehnen_2009, title={Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms},
volume={17}, DOI={10.1162/evco.2009.17.4.17403},
number={4}, journal={Evolutionary Computation}, author={Trautmann, Heike and Wagner,
T. and Naujoks, B. and Preuss, M. and Mehnen, J.}, year={2009}, pages={493–509}
}'
chicago: 'Trautmann, Heike, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen. “Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.”
Evolutionary Computation 17, no. 4 (2009): 493–509. https://doi.org/10.1162/evco.2009.17.4.17403.'
ieee: 'H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen, “Statistical
Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms,”
Evolutionary Computation, vol. 17, no. 4, pp. 493–509, 2009, doi: 10.1162/evco.2009.17.4.17403.'
mla: Trautmann, Heike, et al. “Statistical Methods for Convergence Detection of
Multi-Objective Evolutionary Algorithms.” Evolutionary Computation, vol.
17, no. 4, 2009, pp. 493–509, doi:10.1162/evco.2009.17.4.17403.
short: H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, J. Mehnen, Evolutionary Computation
17 (2009) 493–509.
date_created: 2023-08-04T16:19:21Z
date_updated: 2024-06-10T11:55:57Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco.2009.17.4.17403
intvolume: ' 17'
issue: '4'
language:
- iso: eng
page: 493-509
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Statistical Methods for Convergence Detection of Multi-Objective Evolutionary
Algorithms
type: journal_article
user_id: '15504'
volume: 17
year: '2009'
...
---
_id: '46419'
author:
- 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: 'Mehnen J, Trautmann H. Robust Multi-objective Optimisation of Weld Bead Geometry
for Additive Manufacturing. In: Teti R, ed. Proceedings of the 6$^th$ CIRP
International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’08). Copyright C.O.C. Com. org. Conv.; 2008.'
apa: Mehnen, J., & Trautmann, H. (2008). Robust Multi-objective Optimisation
of Weld Bead Geometry for Additive Manufacturing. In R. Teti (Ed.), Proceedings
of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing
Engineering (CIRP ICME ’08). Copyright C.O.C. Com. org. Conv.
bibtex: '@inproceedings{Mehnen_Trautmann_2008, place={Naples, Italy}, title={Robust
Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing},
booktitle={Proceedings of the 6$^th$ CIRP International Seminar on Intelligent
Computation in Manufacturing Engineering (CIRP ICME ’08)}, publisher={Copyright
C.O.C. Com. org. Conv.}, author={Mehnen, J and Trautmann, Heike}, editor={Teti,
R}, year={2008} }'
chicago: 'Mehnen, J, and Heike Trautmann. “Robust Multi-Objective Optimisation of
Weld Bead Geometry for Additive Manufacturing.” In Proceedings of the 6$^th$
CIRP International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’08), edited by R Teti. Naples, Italy: Copyright C.O.C. Com. org.
Conv., 2008.'
ieee: J. Mehnen and H. Trautmann, “Robust Multi-objective Optimisation of Weld Bead
Geometry for Additive Manufacturing,” in Proceedings of the 6$^th$ CIRP International
Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08),
2008.
mla: Mehnen, J., and Heike Trautmann. “Robust Multi-Objective Optimisation of Weld
Bead Geometry for Additive Manufacturing.” Proceedings of the 6$^th$ CIRP International
Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08),
edited by R Teti, Copyright C.O.C. Com. org. Conv., 2008.
short: 'J. Mehnen, H. Trautmann, in: R. Teti (Ed.), Proceedings of the 6$^th$ CIRP
International Seminar on Intelligent Computation in Manufacturing Engineering
(CIRP ICME ’08), Copyright C.O.C. Com. org. Conv., Naples, Italy, 2008.'
date_created: 2023-08-04T16:19:46Z
date_updated: 2023-10-16T14:00:12Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Teti, R
last_name: Teti
language:
- iso: eng
place: Naples, Italy
publication: Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation
in Manufacturing Engineering (CIRP ICME ’08)
publisher: Copyright C.O.C. Com. org. Conv.
status: public
title: Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing
type: conference
user_id: '15504'
year: '2008'
...
---
_id: '46420'
abstract:
- lang: eng
text: A systematic approach for determining the generation number at which a specific
Multi-Objective Evolutionary Algorithm (MOEA) has converged for a given optimization
problem is introduced. Convergence is measured by the performance indicators Generational
Distance, Spread and Hypervolume. The stochastic nature of the MOEA is taken into
account by repeated runs per generation number which results in a highly robust
procedure. For each generation number the MOEA is repeated a fixed number of times,
and the Kolmogorow-Smirnov-Test is used in order to decide if a significant change
in performance is gained in comparison to preceding generations. A comparison
of different MOEAs on a problem with respect to necessary generation numbers becomes
possible, and the understanding of the algorithm’s behaviour is supported by analysing
the development of the indicator values. The procedure is illustrated by means
of standard test problems.
author:
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Uwe
full_name: Ligges, Uwe
last_name: Ligges
- first_name: Jörn
full_name: Mehnen, Jörn
last_name: Mehnen
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
citation:
ama: 'Trautmann H, Ligges U, Mehnen J, Preuss M. A Convergence Criterion for Multiobjective
Evolutionary Algorithms Based on Systematic Statistical Testing. In: Rudolph G,
Jansen T, Beume N, Lucas S, Poloni C, eds. Parallel Problem Solving from Nature
– PPSN X. Springer Berlin Heidelberg; 2008:825–836.'
apa: Trautmann, H., Ligges, U., Mehnen, J., & Preuss, M. (2008). A Convergence
Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical
Testing. In G. Rudolph, T. Jansen, N. Beume, S. Lucas, & C. Poloni (Eds.),
Parallel Problem Solving from Nature – PPSN X (pp. 825–836). Springer Berlin
Heidelberg.
bibtex: '@inproceedings{Trautmann_Ligges_Mehnen_Preuss_2008, place={Berlin, Heidelberg},
title={A Convergence Criterion for Multiobjective Evolutionary Algorithms Based
on Systematic Statistical Testing}, booktitle={Parallel Problem Solving from Nature
– PPSN X}, publisher={Springer Berlin Heidelberg}, author={Trautmann, Heike and
Ligges, Uwe and Mehnen, Jörn and Preuss, Mike}, editor={Rudolph, Günter and Jansen,
Thomas and Beume, Nicola and Lucas, Simon and Poloni, Carlo}, year={2008}, pages={825–836}
}'
chicago: 'Trautmann, Heike, Uwe Ligges, Jörn Mehnen, and Mike Preuss. “A Convergence
Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical
Testing.” In Parallel Problem Solving from Nature – PPSN X, edited by Günter
Rudolph, Thomas Jansen, Nicola Beume, Simon Lucas, and Carlo Poloni, 825–836.
Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.'
ieee: H. Trautmann, U. Ligges, J. Mehnen, and M. Preuss, “A Convergence Criterion
for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing,”
in Parallel Problem Solving from Nature – PPSN X, 2008, pp. 825–836.
mla: Trautmann, Heike, et al. “A Convergence Criterion for Multiobjective Evolutionary
Algorithms Based on Systematic Statistical Testing.” Parallel Problem Solving
from Nature – PPSN X, edited by Günter Rudolph et al., Springer Berlin Heidelberg,
2008, pp. 825–836.
short: 'H. Trautmann, U. Ligges, J. Mehnen, M. Preuss, in: G. Rudolph, T. Jansen,
N. Beume, S. Lucas, C. Poloni (Eds.), Parallel Problem Solving from Nature – PPSN
X, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 825–836.'
date_created: 2023-08-04T16:20:35Z
date_updated: 2024-06-10T11:55:46Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Thomas
full_name: Jansen, Thomas
last_name: Jansen
- first_name: Nicola
full_name: Beume, Nicola
last_name: Beume
- first_name: Simon
full_name: Lucas, Simon
last_name: Lucas
- first_name: Carlo
full_name: Poloni, Carlo
last_name: Poloni
language:
- iso: eng
page: 825–836
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature – PPSN X
publication_identifier:
isbn:
- 978-3-540-87700-4
publisher: Springer Berlin Heidelberg
status: public
title: A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on
Systematic Statistical Testing
type: conference
user_id: '15504'
year: '2008'
...
---
_id: '46421'
abstract:
- lang: eng
text: Multi-objective evolutionary algorithms (MOEAs) are generally designed to
find a well spread Pareto-front approximation. Often, only a small section of
this front may be of practical interest. Desirability functions (DFs) are able
to describe user preferences intuitively. Furthermore, DFs can be attached to
any fitness function easily. This way, desirability functions can help in guiding
MOEAs without introducing additional restrictions or changes to the algorithm.
The application of noisy fitness functions is not straight forward but relevant
to many real-world problems. Therefore, a variant of Harrington’s one-sided desirability
function using expectations is introduced which takes noise into account. A deterministic
strategy as well as the XSGA-II are used in combination with DF to solve a noisy
Binh problem and a noisy cost estimation problem for turning processes.
author:
- first_name: Jorn
full_name: Mehnen, Jorn
last_name: Mehnen
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Ashutosh
full_name: Tiwari, Ashutosh
last_name: Tiwari
citation:
ama: 'Mehnen J, Trautmann H, Tiwari A. Introducing user preference using Desirability
Functions in Multi-Objective Evolutionary Optimisation of noisy processes. In:
2007 IEEE Congress on Evolutionary Computation. ; 2007:2687-2694. doi:10.1109/CEC.2007.4424810'
apa: Mehnen, J., Trautmann, H., & Tiwari, A. (2007). Introducing user preference
using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy
processes. 2007 IEEE Congress on Evolutionary Computation, 2687–2694. https://doi.org/10.1109/CEC.2007.4424810
bibtex: '@inproceedings{Mehnen_Trautmann_Tiwari_2007, title={Introducing user preference
using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy
processes}, DOI={10.1109/CEC.2007.4424810},
booktitle={2007 IEEE Congress on Evolutionary Computation}, author={Mehnen, Jorn
and Trautmann, Heike and Tiwari, Ashutosh}, year={2007}, pages={2687–2694} }'
chicago: Mehnen, Jorn, Heike Trautmann, and Ashutosh Tiwari. “Introducing User Preference
Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy
Processes.” In 2007 IEEE Congress on Evolutionary Computation, 2687–94,
2007. https://doi.org/10.1109/CEC.2007.4424810.
ieee: 'J. Mehnen, H. Trautmann, and A. Tiwari, “Introducing user preference using
Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes,”
in 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 2687–2694,
doi: 10.1109/CEC.2007.4424810.'
mla: Mehnen, Jorn, et al. “Introducing User Preference Using Desirability Functions
in Multi-Objective Evolutionary Optimisation of Noisy Processes.” 2007 IEEE
Congress on Evolutionary Computation, 2007, pp. 2687–94, doi:10.1109/CEC.2007.4424810.
short: 'J. Mehnen, H. Trautmann, A. Tiwari, in: 2007 IEEE Congress on Evolutionary
Computation, 2007, pp. 2687–2694.'
date_created: 2023-08-04T16:21:27Z
date_updated: 2023-10-16T14:00:44Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2007.4424810
language:
- iso: eng
page: 2687-2694
publication: 2007 IEEE Congress on Evolutionary Computation
publication_identifier:
issn:
- 1941-0026
status: public
title: Introducing user preference using Desirability Functions in Multi-Objective
Evolutionary Optimisation of noisy processes
type: conference
user_id: '15504'
year: '2007'
...
---
_id: '46423'
abstract:
- lang: eng
text: The concept of desirability is a means for complexity reduction of multivariate
quality optimization. This paper provides a theoretical breakthrough regarding
desirability indices, which application fields were formerly limited primarily
by the lack of its distribution. Focussed are the distributions of Harrington’s
desirability functions and different types of the desirability index.
author:
- 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: Trautmann H, Weihs C. On the Distribution of the Desirability Index using Harrington’s
Desirability Function. Metrika. 2006;63(2):207–213. doi:10.1007/s00184-005-0012-0
apa: Trautmann, H., & Weihs, C. (2006). On the Distribution of the Desirability
Index using Harrington’s Desirability Function. Metrika, 63(2),
207–213. https://doi.org/10.1007/s00184-005-0012-0
bibtex: '@article{Trautmann_Weihs_2006, title={On the Distribution of the Desirability
Index using Harrington’s Desirability Function}, volume={63}, DOI={10.1007/s00184-005-0012-0},
number={2}, journal={Metrika}, author={Trautmann, Heike and Weihs, C}, year={2006},
pages={207–213} }'
chicago: 'Trautmann, Heike, and C Weihs. “On the Distribution of the Desirability
Index Using Harrington’s Desirability Function.” Metrika 63, no. 2 (2006):
207–213. https://doi.org/10.1007/s00184-005-0012-0.'
ieee: 'H. Trautmann and C. Weihs, “On the Distribution of the Desirability Index
using Harrington’s Desirability Function,” Metrika, vol. 63, no. 2, pp.
207–213, 2006, doi: 10.1007/s00184-005-0012-0.'
mla: Trautmann, Heike, and C. Weihs. “On the Distribution of the Desirability Index
Using Harrington’s Desirability Function.” Metrika, vol. 63, no. 2, 2006,
pp. 207–213, doi:10.1007/s00184-005-0012-0.
short: H. Trautmann, C. Weihs, Metrika 63 (2006) 207–213.
date_created: 2023-08-04T16:22:48Z
date_updated: 2023-10-04T22:25:10Z
doi: 10.1007/s00184-005-0012-0
intvolume: ' 63'
issue: '2'
language:
- iso: eng
page: 207–213
publication: Metrika
status: public
title: On the Distribution of the Desirability Index using Harrington’s Desirability
Function
type: journal_article
user_id: '100740'
volume: 63
year: '2006'
...
---
_id: '46422'
author:
- 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: 'Mehnen J, Trautmann H. Integration of Expert’s Preferences in Pareto Optimization
by Desirability Function Techniques. In: Teti R, ed. CIRP ICME ’06) — Proceedings
of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing
Engineering. C.O.C. Com. org. Conv. CIRP ICME ’06; 2006:293–298.'
apa: Mehnen, J., & Trautmann, H. (2006). Integration of Expert’s Preferences
in Pareto Optimization by Desirability Function Techniques. In R. Teti (Ed.),
CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent
Computation in Manufacturing Engineering (pp. 293–298). C.O.C. Com. org. Conv.
CIRP ICME ’06.
bibtex: '@inproceedings{Mehnen_Trautmann_2006, place={Ischia, Italy}, title={Integration
of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques},
booktitle={CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar
on Intelligent Computation in Manufacturing Engineering}, publisher={C.O.C. Com.
org. Conv. CIRP ICME ’06}, author={Mehnen, J and Trautmann, Heike}, editor={Teti,
R}, year={2006}, pages={293–298} }'
chicago: 'Mehnen, J, and Heike Trautmann. “Integration of Expert’s Preferences in
Pareto Optimization by Desirability Function Techniques.” In CIRP ICME ’06)
— Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation
in Manufacturing Engineering, edited by R Teti, 293–298. Ischia, Italy: C.O.C.
Com. org. Conv. CIRP ICME ’06, 2006.'
ieee: J. Mehnen and H. Trautmann, “Integration of Expert’s Preferences in Pareto
Optimization by Desirability Function Techniques,” in CIRP ICME ’06) — Proceedings
of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing
Engineering, 2006, pp. 293–298.
mla: Mehnen, J., and Heike Trautmann. “Integration of Expert’s Preferences in Pareto
Optimization by Desirability Function Techniques.” CIRP ICME ’06) — Proceedings
of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing
Engineering, edited by R Teti, C.O.C. Com. org. Conv. CIRP ICME ’06, 2006,
pp. 293–298.
short: 'J. Mehnen, H. Trautmann, in: R. Teti (Ed.), CIRP ICME ’06) — Proceedings
of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing
Engineering, C.O.C. Com. org. Conv. CIRP ICME ’06, Ischia, Italy, 2006, pp. 293–298.'
date_created: 2023-08-04T16:22:05Z
date_updated: 2023-10-16T14:00:58Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Teti, R
last_name: Teti
language:
- iso: eng
page: 293–298
place: Ischia, Italy
publication: CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar
on Intelligent Computation in Manufacturing Engineering
publisher: C.O.C. Com. org. Conv. CIRP ICME ’06
status: public
title: Integration of Expert’s Preferences in Pareto Optimization by Desirability
Function Techniques
type: conference
user_id: '15504'
year: '2006'
...