---
_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'
...
---
_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'
...