---
_id: '29940'
abstract:
- lang: eng
text: A full-bridge modular multilevel converter (MMC) is compared to a half-bridge-based
MMC for high-current low-voltage DC-applications such as electrolysis, arc welding
or datacenters with DC-power distribution. Usually, modular multilevel converters
are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but
to meet the needs of a high-current demand at low output voltage levels, the modular
converter concept requires adaptations. In the proposed concept, the MMC is used
to step-down the three-phase medium-voltage of 10 kV. Therefore, each module is
extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage
range of 142-220V and to provide galvanic isolation. The proposed MMC converter
with full-bridge modules uses half the number of modules compared to a half-bridge-based
MMC while reducing the voltage ripple by 78% and capacitor losses by 64% by rearranging
the same components to ensure identical costs and volume. For additional reliability,
a new robust algorithm for balancing conduction losses during the bypass phase
is presented.
author:
- first_name: Roland
full_name: Unruh, Roland
id: '34289'
last_name: Unruh
- first_name: Frank
full_name: Schafmeister, Frank
id: '71291'
last_name: Schafmeister
- first_name: Norbert
full_name: Fröhleke, Norbert
last_name: Fröhleke
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Unruh R, Schafmeister F, Fröhleke N, Böcker J. 1-MW Full-Bridge MMC for High-Current
Low-Voltage (100V-400V) DC-Applications. In: PCIM Europe Digital Days 2020;
International Exhibition and Conference for Power Electronics, Intelligent Motion,
Renewable Energy and Energy Management. VDE; 2020.'
apa: Unruh, R., Schafmeister, F., Fröhleke, N., & Böcker, J. (2020). 1-MW Full-Bridge
MMC for High-Current Low-Voltage (100V-400V) DC-Applications. PCIM Europe Digital
Days 2020; International Exhibition and Conference for Power Electronics, Intelligent
Motion, Renewable Energy and Energy Management. PCIM Europe digital days 2020,
Germany.
bibtex: '@inproceedings{Unruh_Schafmeister_Fröhleke_Böcker_2020, title={1-MW Full-Bridge
MMC for High-Current Low-Voltage (100V-400V) DC-Applications}, booktitle={PCIM
Europe digital days 2020; International Exhibition and Conference for Power Electronics,
Intelligent Motion, Renewable Energy and Energy Management}, publisher={VDE},
author={Unruh, Roland and Schafmeister, Frank and Fröhleke, Norbert and Böcker,
Joachim}, year={2020} }'
chicago: Unruh, Roland, Frank Schafmeister, Norbert Fröhleke, and Joachim Böcker.
“1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V) DC-Applications.”
In PCIM Europe Digital Days 2020; International Exhibition and Conference for
Power Electronics, Intelligent Motion, Renewable Energy and Energy Management.
VDE, 2020.
ieee: R. Unruh, F. Schafmeister, N. Fröhleke, and J. Böcker, “1-MW Full-Bridge MMC
for High-Current Low-Voltage (100V-400V) DC-Applications,” presented at the PCIM
Europe digital days 2020, Germany, 2020.
mla: Unruh, Roland, et al. “1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V)
DC-Applications.” PCIM Europe Digital Days 2020; International Exhibition and
Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy
Management, VDE, 2020.
short: 'R. Unruh, F. Schafmeister, N. Fröhleke, J. Böcker, in: PCIM Europe Digital
Days 2020; International Exhibition and Conference for Power Electronics, Intelligent
Motion, Renewable Energy and Energy Management, VDE, 2020.'
conference:
end_date: 2020-07-08
location: Germany
name: PCIM Europe digital days 2020
start_date: 2020-07-07
date_created: 2022-02-21T16:42:30Z
date_updated: 2023-10-20T11:52:39Z
department:
- _id: '52'
keyword:
- Cascaded H-Bridge
- Solid-State Transformer
- Capacitor voltage ripple
- Zero sequence voltage
- Full-Bridge
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/abstract/document/9178138
publication: PCIM Europe digital days 2020; International Exhibition and Conference
for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
publication_identifier:
isbn:
- 978-3-8007-5245-4
publication_status: published
publisher: VDE
status: public
title: 1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V) DC-Applications
type: conference
user_id: '34289'
year: '2020'
...
---
_id: '30001'
abstract:
- lang: eng
text: Heat dissipation is a limiting factor in the performance of many power electronic
components. Especially in the TO-263-7 package, which is used for several SiC-MOSFETs,
the heat transfer must take place through the cross section of the printed circuit
board (PCB) to the heatsink at the bottom side. Most commonly, thermal vias are
used to form this path in a perpendicular direction through all PCB-layers. In
a given soft- and hard switched example applications with the use of C3M0065090J
SiC-MOSFETs, this conventional approach limited the component’s maximum heat dissipation
to approx. 13 W. A recent alternative approach are massive copper blocks (”pedestals”)
being integrated in PCBs and reaching from their top- to the bottom-side in relevant
footprint areas under SMD-housed power semiconductors. Pedestals allowing to increase
the heat dissipation in the given case to even 36 W. This step is achieved due
to the clearly superior heat spreading capability of that massive thermal connection
between SiC-MOSFET and heatsink. For the hard switched example application the
number of switch-elements can be halved to one, by using the pedestal instead
of thermal vias. Independently of optimizing the heat transfer path, the up-front
avoidance of losses helps to stay within existing heat dissipation limits, of
course. The dominant conduction losses of the mentioned soft-switched example
application could be halved by changing to SiC-MOSFET types with significant lowered
RDSon. By using pedestals and changing to SiC-MOSFETs with lowered RDSon, the
number of switch-elements can also be halved for the soft switched application.
author:
- first_name: Benjamin
full_name: Strothmann, Benjamin
id: '22556'
last_name: Strothmann
- first_name: Till
full_name: Piepenbrock, Till
last_name: Piepenbrock
- first_name: Frank
full_name: Schafmeister, Frank
id: '71291'
last_name: Schafmeister
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: 'Strothmann B, Piepenbrock T, Schafmeister F, Böcker J. Heat dissipation strategies
for silicon carbide power SMDs and their use in different applications. In: PCIM
Europe Digital Days 2020; International Exhibition and Conference for Power Electronics,
Intelligent Motion, Renewable Energy and Energy Management. ; 2020:1-7.'
apa: Strothmann, B., Piepenbrock, T., Schafmeister, F., & Böcker, J. (2020).
Heat dissipation strategies for silicon carbide power SMDs and their use in different
applications. PCIM Europe Digital Days 2020; International Exhibition and Conference
for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management,
1–7.
bibtex: '@inproceedings{Strothmann_Piepenbrock_Schafmeister_Böcker_2020, title={Heat
dissipation strategies for silicon carbide power SMDs and their use in different
applications}, booktitle={PCIM Europe digital days 2020; International Exhibition
and Conference for Power Electronics, Intelligent Motion, Renewable Energy and
Energy Management}, author={Strothmann, Benjamin and Piepenbrock, Till and Schafmeister,
Frank and Böcker, Joachim}, year={2020}, pages={1–7} }'
chicago: Strothmann, Benjamin, Till Piepenbrock, Frank Schafmeister, and Joachim
Böcker. “Heat Dissipation Strategies for Silicon Carbide Power SMDs and Their
Use in Different Applications.” In PCIM Europe Digital Days 2020; International
Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable
Energy and Energy Management, 1–7, 2020.
ieee: B. Strothmann, T. Piepenbrock, F. Schafmeister, and J. Böcker, “Heat dissipation
strategies for silicon carbide power SMDs and their use in different applications,”
in PCIM Europe digital days 2020; International Exhibition and Conference for
Power Electronics, Intelligent Motion, Renewable Energy and Energy Management,
2020, pp. 1–7.
mla: Strothmann, Benjamin, et al. “Heat Dissipation Strategies for Silicon Carbide
Power SMDs and Their Use in Different Applications.” PCIM Europe Digital Days
2020; International Exhibition and Conference for Power Electronics, Intelligent
Motion, Renewable Energy and Energy Management, 2020, pp. 1–7.
short: 'B. Strothmann, T. Piepenbrock, F. Schafmeister, J. Böcker, in: PCIM Europe
Digital Days 2020; International Exhibition and Conference for Power Electronics,
Intelligent Motion, Renewable Energy and Energy Management, 2020, pp. 1–7.'
date_created: 2022-02-23T14:14:58Z
date_updated: 2023-10-20T12:23:18Z
department:
- _id: '52'
language:
- iso: eng
page: 1-7
publication: PCIM Europe digital days 2020; International Exhibition and Conference
for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
publication_status: published
status: public
title: Heat dissipation strategies for silicon carbide power SMDs and their use in
different applications
type: conference
user_id: '66'
year: '2020'
...
---
_id: '45384'
author:
- first_name: Jennifer
full_name: Dröse, Jennifer
id: '85820'
last_name: Dröse
citation:
ama: 'Dröse J. Verstehensgrundlagen diagnostizieren - Welche Wissenselemente fokussieren
Lehrkräfte? In: Siller H-S, Weigel W, Wöler JF, eds. Beiträge zum Mathematikunterricht
2020 . WTM; 2020:233-236.'
apa: Dröse, J. (2020). Verstehensgrundlagen diagnostizieren - Welche Wissenselemente
fokussieren Lehrkräfte? In H.-S. Siller, W. Weigel, & J. F. Wöler (Eds.),
Beiträge zum Mathematikunterricht 2020 (pp. 233–236). WTM.
bibtex: '@inproceedings{Dröse_2020, place={Münster}, title={Verstehensgrundlagen
diagnostizieren - Welche Wissenselemente fokussieren Lehrkräfte?}, booktitle={Beiträge
zum Mathematikunterricht 2020 }, publisher={WTM}, author={Dröse, Jennifer}, editor={Siller,
H.-S. and Weigel, W. and Wöler, J. F.}, year={2020}, pages={233–236} }'
chicago: 'Dröse, Jennifer. “Verstehensgrundlagen diagnostizieren - Welche Wissenselemente
fokussieren Lehrkräfte?” In Beiträge zum Mathematikunterricht 2020 , edited
by H.-S. Siller, W. Weigel, and J. F. Wöler, 233–36. Münster: WTM, 2020.'
ieee: J. Dröse, “Verstehensgrundlagen diagnostizieren - Welche Wissenselemente fokussieren
Lehrkräfte?,” in Beiträge zum Mathematikunterricht 2020 , 2020, pp. 233–236.
mla: Dröse, Jennifer. “Verstehensgrundlagen diagnostizieren - Welche Wissenselemente
fokussieren Lehrkräfte?” Beiträge zum Mathematikunterricht 2020 , edited
by H.-S. Siller et al., WTM, 2020, pp. 233–36.
short: 'J. Dröse, in: H.-S. Siller, W. Weigel, J.F. Wöler (Eds.), Beiträge zum Mathematikunterricht
2020 , WTM, Münster, 2020, pp. 233–236.'
date_created: 2023-05-31T07:18:51Z
date_updated: 2023-11-02T08:09:21Z
department:
- _id: '98'
editor:
- first_name: H.-S.
full_name: Siller, H.-S.
last_name: Siller
- first_name: W.
full_name: Weigel, W.
last_name: Weigel
- first_name: J. F.
full_name: Wöler, J. F.
last_name: Wöler
language:
- iso: ger
page: 233-236
place: Münster
publication: 'Beiträge zum Mathematikunterricht 2020 '
publisher: WTM
status: public
title: Verstehensgrundlagen diagnostizieren - Welche Wissenselemente fokussieren Lehrkräfte?
type: conference
user_id: '85820'
year: '2020'
...
---
_id: '45386'
author:
- first_name: Jennifer
full_name: Dröse, Jennifer
id: '85820'
last_name: Dröse
- first_name: V.
full_name: Eisen, V.
last_name: Eisen
- first_name: Susanne
full_name: Prediger, Susanne
last_name: Prediger
- first_name: M.
full_name: Altieri, M.
last_name: Altieri
- first_name: M.
full_name: Schellenbach, M.
last_name: Schellenbach
- first_name: R.
full_name: Menning, R.
last_name: Menning
citation:
ama: 'Dröse J, Eisen V, Prediger S, Altieri M, Schellenbach M, Menning R. Textaufgaben
lesen lernen – eine digital gestützte Einheit mit App . In: Mathematik lehren
223. ; 2020:38-40.'
apa: Dröse, J., Eisen, V., Prediger, S., Altieri, M., Schellenbach, M., & Menning,
R. (2020). Textaufgaben lesen lernen – eine digital gestützte Einheit mit App
. In Mathematik lehren 223 (pp. 38–40).
bibtex: '@inbook{Dröse_Eisen_Prediger_Altieri_Schellenbach_Menning_2020, title={Textaufgaben
lesen lernen – eine digital gestützte Einheit mit App }, booktitle={Mathematik
lehren 223}, author={Dröse, Jennifer and Eisen, V. and Prediger, Susanne and Altieri,
M. and Schellenbach, M. and Menning, R.}, year={2020}, pages={38–40} }'
chicago: Dröse, Jennifer, V. Eisen, Susanne Prediger, M. Altieri, M. Schellenbach,
and R. Menning. “Textaufgaben lesen lernen – eine digital gestützte Einheit mit
App .” In Mathematik lehren 223, 38–40, 2020.
ieee: J. Dröse, V. Eisen, S. Prediger, M. Altieri, M. Schellenbach, and R. Menning,
“Textaufgaben lesen lernen – eine digital gestützte Einheit mit App ,” in Mathematik
lehren 223, 2020, pp. 38–40.
mla: Dröse, Jennifer, et al. “Textaufgaben lesen lernen – eine digital gestützte
Einheit mit App .” Mathematik lehren 223, 2020, pp. 38–40.
short: 'J. Dröse, V. Eisen, S. Prediger, M. Altieri, M. Schellenbach, R. Menning,
in: Mathematik lehren 223, 2020, pp. 38–40.'
date_created: 2023-05-31T07:42:06Z
date_updated: 2023-11-02T08:09:54Z
department:
- _id: '98'
language:
- iso: ger
page: 38-40
publication: Mathematik lehren 223
status: public
title: 'Textaufgaben lesen lernen – eine digital gestützte Einheit mit App '
type: book_chapter
user_id: '85820'
year: '2020'
...
---
_id: '20766'
abstract:
- lang: eng
text: Recently, the source separation performance was greatly improved by time-domain
audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN
is a simple but effective model for a long sequential data. While DPRNN is quite
efficient in modeling a sequential data of the length of an utterance, i.e., about
5 to 10 second data, it is harder to apply it to longer sequences such as whole
conversations consisting of multiple utterances. It is simply because, in such
a case, the number of time steps consumed by its internal module called inter-chunk
RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path
RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical
manner. In the MPRNN framework, the input data is represented at several (>_ 3)
time-resolutions, each of which is modeled by a specific RNN sub-module. For example,
the RNN sub-module that deals with the finest resolution may model temporal relationship
only within a phoneme, while the RNN sub-module handling the most coarse resolution
may capture only the relationship between utterances such as speaker information.
We perform experiments using simulated dialogue-like mixtures and show that MPRNN
has greater model capacity, and it outperforms the current state-of-the-art DPRNN
framework especially in online processing scenarios.
author:
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Kinoshita K, von Neumann T, Delcroix M, Nakatani T, Haeb-Umbach R. Multi-Path
RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker
Stream Separation. In: Proc. Interspeech 2020. ; 2020:2652-2656. doi:10.21437/Interspeech.2020-2388'
apa: Kinoshita, K., von Neumann, T., Delcroix, M., Nakatani, T., & Haeb-Umbach,
R. (2020). Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and
its Application to Speaker Stream Separation. Proc. Interspeech 2020, 2652–2656.
https://doi.org/10.21437/Interspeech.2020-2388
bibtex: '@inproceedings{Kinoshita_von Neumann_Delcroix_Nakatani_Haeb-Umbach_2020,
title={Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its
Application to Speaker Stream Separation}, DOI={10.21437/Interspeech.2020-2388},
booktitle={Proc. Interspeech 2020}, author={Kinoshita, Keisuke and von Neumann,
Thilo and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}, year={2020},
pages={2652–2656} }'
chicago: Kinoshita, Keisuke, Thilo von Neumann, Marc Delcroix, Tomohiro Nakatani,
and Reinhold Haeb-Umbach. “Multi-Path RNN for Hierarchical Modeling of Long Sequential
Data and Its Application to Speaker Stream Separation.” In Proc. Interspeech
2020, 2652–56, 2020. https://doi.org/10.21437/Interspeech.2020-2388.
ieee: 'K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, and R. Haeb-Umbach,
“Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application
to Speaker Stream Separation,” in Proc. Interspeech 2020, 2020, pp. 2652–2656,
doi: 10.21437/Interspeech.2020-2388.'
mla: Kinoshita, Keisuke, et al. “Multi-Path RNN for Hierarchical Modeling of Long
Sequential Data and Its Application to Speaker Stream Separation.” Proc. Interspeech
2020, 2020, pp. 2652–56, doi:10.21437/Interspeech.2020-2388.
short: 'K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, R. Haeb-Umbach,
in: Proc. Interspeech 2020, 2020, pp. 2652–2656.'
date_created: 2020-12-16T14:15:24Z
date_updated: 2023-11-15T12:14:25Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.21437/Interspeech.2020-2388
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T14:16:32Z
date_updated: 2020-12-16T14:16:32Z
file_id: '20767'
file_name: INTERSPEECH_2020_vonNeumann1_Paper.pdf
file_size: 1725219
relation: main_file
file_date_updated: 2020-12-16T14:16:32Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 2652-2656
publication: Proc. Interspeech 2020
quality_controlled: '1'
status: public
title: Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application
to Speaker Stream Separation
type: conference
user_id: '49870'
year: '2020'
...
---
_id: '17994'
abstract:
- lang: eng
text: In this work we review the novel framework for the computation of finite dimensional
invariant sets of infinite dimensional dynamical systems developed in [6] and
[36]. By utilizing results on embedding techniques for infinite dimensional systems
we extend a classical subdivision scheme [8] as well as a continuation algorithm
[7] for the computation of attractors and invariant manifolds of finite dimensional
systems to the infinite dimensional case. We show how to implement this approach
for the analysis of delay differential equations and partial differential equations
and illustrate the feasibility of our implementation by computing the attractor
of the Mackey-Glass equation and the unstable manifold of the one-dimensional
Kuramoto-Sivashinsky equation.
author:
- first_name: Raphael
full_name: Gerlach, Raphael
id: '32655'
last_name: Gerlach
- first_name: Adrian
full_name: Ziessler, Adrian
last_name: Ziessler
citation:
ama: 'Gerlach R, Ziessler A. The Approximation of Invariant Sets in Infinite Dimensional
Dynamical Systems. In: Junge O, Schütze O, Ober-Blöbaum S, Padberg-Gehle K, eds.
Advances in Dynamics, Optimization and Computation. Vol 304. Studies in
Systems, Decision and Control. Springer International Publishing; 2020:66-85.
doi:10.1007/978-3-030-51264-4_3'
apa: Gerlach, R., & Ziessler, A. (2020). The Approximation of Invariant Sets
in Infinite Dimensional Dynamical Systems. In O. Junge, O. Schütze, S. Ober-Blöbaum,
& K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation
(Vol. 304, pp. 66–85). Springer International Publishing. https://doi.org/10.1007/978-3-030-51264-4_3
bibtex: '@inbook{Gerlach_Ziessler_2020, place={Cham}, series={Studies in Systems,
Decision and Control}, title={The Approximation of Invariant Sets in Infinite
Dimensional Dynamical Systems}, volume={304}, DOI={10.1007/978-3-030-51264-4_3},
booktitle={Advances in Dynamics, Optimization and Computation}, publisher={Springer
International Publishing}, author={Gerlach, Raphael and Ziessler, Adrian}, editor={Junge,
Oliver and Schütze, Oliver and Ober-Blöbaum, Sina and Padberg-Gehle, Kathrin},
year={2020}, pages={66–85}, collection={Studies in Systems, Decision and Control}
}'
chicago: 'Gerlach, Raphael, and Adrian Ziessler. “The Approximation of Invariant
Sets in Infinite Dimensional Dynamical Systems.” In Advances in Dynamics, Optimization
and Computation, edited by Oliver Junge, Oliver Schütze, Sina Ober-Blöbaum,
and Kathrin Padberg-Gehle, 304:66–85. Studies in Systems, Decision and Control.
Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-51264-4_3.'
ieee: 'R. Gerlach and A. Ziessler, “The Approximation of Invariant Sets in Infinite
Dimensional Dynamical Systems,” in Advances in Dynamics, Optimization and Computation,
vol. 304, O. Junge, O. Schütze, S. Ober-Blöbaum, and K. Padberg-Gehle, Eds. Cham:
Springer International Publishing, 2020, pp. 66–85.'
mla: Gerlach, Raphael, and Adrian Ziessler. “The Approximation of Invariant Sets
in Infinite Dimensional Dynamical Systems.” Advances in Dynamics, Optimization
and Computation, edited by Oliver Junge et al., vol. 304, Springer International
Publishing, 2020, pp. 66–85, doi:10.1007/978-3-030-51264-4_3.
short: 'R. Gerlach, A. Ziessler, in: O. Junge, O. Schütze, S. Ober-Blöbaum, K. Padberg-Gehle
(Eds.), Advances in Dynamics, Optimization and Computation, Springer International
Publishing, Cham, 2020, pp. 66–85.'
date_created: 2020-08-14T15:02:22Z
date_updated: 2023-11-17T13:13:25Z
department:
- _id: '101'
doi: 10.1007/978-3-030-51264-4_3
editor:
- first_name: Oliver
full_name: Junge, Oliver
last_name: Junge
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
- first_name: Sina
full_name: Ober-Blöbaum, Sina
last_name: Ober-Blöbaum
- first_name: Kathrin
full_name: Padberg-Gehle, Kathrin
last_name: Padberg-Gehle
intvolume: ' 304'
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/chapter/10.1007/978-3-030-51264-4_3
page: 66-85
place: Cham
publication: Advances in Dynamics, Optimization and Computation
publication_identifier:
isbn:
- '9783030512637'
- '9783030512644'
issn:
- 2198-4182
- 2198-4190
publication_status: published
publisher: Springer International Publishing
series_title: Studies in Systems, Decision and Control
status: public
title: The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems
type: book_chapter
user_id: '32655'
volume: 304
year: '2020'
...
---
_id: '16712'
abstract:
- lang: eng
text: We investigate self-adjoint matrices A∈Rn,n with respect to their equivariance
properties. We show in particular that a matrix is self-adjoint if and only if
it is equivariant with respect to the action of a group Γ2(A)⊂O(n) which is isomorphic
to ⊗nk=1Z2. If the self-adjoint matrix possesses multiple eigenvalues – this may,
for instance, be induced by symmetry properties of an underlying dynamical system
– then A is even equivariant with respect to the action of a group Γ(A)≃∏ki=1O(mi)
where m1,…,mk are the multiplicities of the eigenvalues λ1,…,λk of A. We discuss
implications of this result for equivariant bifurcation problems, and we briefly
address further applications for the Procrustes problem, graph symmetries and
Taylor expansions.
author:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Raphael
full_name: Gerlach, Raphael
id: '32655'
last_name: Gerlach
- first_name: Stefan
full_name: Klus, Stefan
last_name: Klus
citation:
ama: Dellnitz M, Gebken B, Gerlach R, Klus S. On the equivariance properties of
self-adjoint matrices. Dynamical Systems. 2020;35(2):197-215. doi:10.1080/14689367.2019.1661355
apa: Dellnitz, M., Gebken, B., Gerlach, R., & Klus, S. (2020). On the equivariance
properties of self-adjoint matrices. Dynamical Systems, 35(2), 197–215.
https://doi.org/10.1080/14689367.2019.1661355
bibtex: '@article{Dellnitz_Gebken_Gerlach_Klus_2020, title={On the equivariance
properties of self-adjoint matrices}, volume={35}, DOI={10.1080/14689367.2019.1661355},
number={2}, journal={Dynamical Systems}, author={Dellnitz, Michael and Gebken,
Bennet and Gerlach, Raphael and Klus, Stefan}, year={2020}, pages={197–215} }'
chicago: 'Dellnitz, Michael, Bennet Gebken, Raphael Gerlach, and Stefan Klus. “On
the Equivariance Properties of Self-Adjoint Matrices.” Dynamical Systems
35, no. 2 (2020): 197–215. https://doi.org/10.1080/14689367.2019.1661355.'
ieee: 'M. Dellnitz, B. Gebken, R. Gerlach, and S. Klus, “On the equivariance properties
of self-adjoint matrices,” Dynamical Systems, vol. 35, no. 2, pp. 197–215,
2020, doi: 10.1080/14689367.2019.1661355.'
mla: Dellnitz, Michael, et al. “On the Equivariance Properties of Self-Adjoint Matrices.”
Dynamical Systems, vol. 35, no. 2, 2020, pp. 197–215, doi:10.1080/14689367.2019.1661355.
short: M. Dellnitz, B. Gebken, R. Gerlach, S. Klus, Dynamical Systems 35 (2020)
197–215.
date_created: 2020-04-16T14:07:25Z
date_updated: 2023-11-17T13:12:59Z
department:
- _id: '101'
doi: 10.1080/14689367.2019.1661355
intvolume: ' 35'
issue: '2'
language:
- iso: eng
main_file_link:
- url: https://doi.org/10.1080/14689367.2019.1661355
page: 197-215
publication: Dynamical Systems
publication_identifier:
issn:
- 1468-9367
- 1468-9375
publication_status: published
status: public
title: On the equivariance properties of self-adjoint matrices
type: journal_article
user_id: '32655'
volume: 35
year: '2020'
...
---
_id: '20753'
abstract:
- lang: eng
text: 'In this paper we present our system for the detection and classification
of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection
and separation in domestic environments. We introduce two new models: the forward-backward
convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional
neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers
sharing the same CNN for preprocessing. With one RNN processing a recording in
forward direction and the other in backward direction, the two networks are trained
to jointly predict audio tags, i.e., weak labels, at each time step within a recording,
given that at each time step they have jointly processed the whole recording.
The proposed training encourages the classifiers to tag events as soon as possible.
Therefore, after training, the networks can be applied to shorter audio segments
of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned
CNN to complement SED. It is trained to predict strong labels while using (predicted)
tags, i.e., weak labels, as additional input. For training pseudo strong labels
from a FBCRNN ensemble are used. The presented system scored the fourth and third
place in the systems and teams rankings, respectively. Subsequent improvements
allow our system to even outperform the challenge baseline and winner systems
in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation
set. Source code is publicly available at https://github.com/fgnt/pb_sed.'
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Haeb-Umbach R. Forward-Backward Convolutional Recurrent Neural Networks
and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised
Sound Event Detection. In: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020). ; 2020.'
apa: Ebbers, J., & Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection. Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020).
bibtex: '@inproceedings{Ebbers_Haeb-Umbach_2020, title={Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection}, booktitle={Proceedings
of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop
(DCASE2020)}, author={Ebbers, Janek and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection.” In Proceedings of the
Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020),
2020.
ieee: J. Ebbers and R. Haeb-Umbach, “Forward-Backward Convolutional Recurrent Neural
Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection,” 2020.
mla: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection.” Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.
short: 'J. Ebbers, R. Haeb-Umbach, in: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.'
date_created: 2020-12-16T08:55:27Z
date_updated: 2023-11-22T08:27:32Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T08:57:22Z
date_updated: 2020-12-16T08:57:22Z
file_id: '20754'
file_name: DCASE2020Workshop_Ebbers_Paper.pdf
file_size: 108326
relation: main_file
file_date_updated: 2020-12-16T08:57:22Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the Detection and Classification of Acoustic Scenes and
Events 2020 Workshop (DCASE2020)
quality_controlled: '1'
status: public
title: Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned
Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection
type: conference
user_id: '34851'
year: '2020'
...
---
_id: '16790'
author:
- first_name: Sarah Claudia
full_name: Krings, Sarah Claudia
id: '64063'
last_name: Krings
orcid: 0000-0001-8040-7553
- first_name: Enes
full_name: Yigitbas, Enes
id: '8447'
last_name: Yigitbas
orcid: 0000-0002-5967-833X
- first_name: Ivan
full_name: Jovanovikj, Ivan
id: '39187'
last_name: Jovanovikj
orcid: https://orcid.org/0000-0002-1838-794X
- first_name: Stefan
full_name: Sauer, Stefan
id: '447'
last_name: Sauer
orcid: 0000-0003-3084-0409
- first_name: Gregor
full_name: Engels, Gregor
id: '107'
last_name: Engels
citation:
ama: 'Krings SC, Yigitbas E, Jovanovikj I, Sauer S, Engels G. Development Framework
for Context-Aware Augmented Reality Applications. In: Proceedings of the 12th
ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020).
; 2020. doi:10.1145/3393672.3398640'
apa: Krings, S. C., Yigitbas, E., Jovanovikj, I., Sauer, S., & Engels, G. (2020).
Development Framework for Context-Aware Augmented Reality Applications. Proceedings
of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
(EICS 2020). https://doi.org/10.1145/3393672.3398640
bibtex: '@inproceedings{Krings_Yigitbas_Jovanovikj_Sauer_Engels_2020, title={Development
Framework for Context-Aware Augmented Reality Applications}, DOI={10.1145/3393672.3398640},
booktitle={Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020)}, author={Krings, Sarah Claudia and Yigitbas, Enes
and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}, year={2020} }'
chicago: Krings, Sarah Claudia, Enes Yigitbas, Ivan Jovanovikj, Stefan Sauer, and
Gregor Engels. “Development Framework for Context-Aware Augmented Reality Applications.”
In Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020), 2020. https://doi.org/10.1145/3393672.3398640.
ieee: 'S. C. Krings, E. Yigitbas, I. Jovanovikj, S. Sauer, and G. Engels, “Development
Framework for Context-Aware Augmented Reality Applications,” 2020, doi: 10.1145/3393672.3398640.'
mla: Krings, Sarah Claudia, et al. “Development Framework for Context-Aware Augmented
Reality Applications.” Proceedings of the 12th ACM SIGCHI Symposium on Engineering
Interactive Computing Systems (EICS 2020), 2020, doi:10.1145/3393672.3398640.
short: 'S.C. Krings, E. Yigitbas, I. Jovanovikj, S. Sauer, G. Engels, in: Proceedings
of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
(EICS 2020), 2020.'
date_created: 2020-04-21T11:49:52Z
date_updated: 2023-12-07T10:42:15Z
department:
- _id: '66'
- _id: '534'
doi: 10.1145/3393672.3398640
language:
- iso: eng
publication: Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive
Computing Systems (EICS 2020)
publication_identifier:
isbn:
- 978-1-4503-7984-7/20/06
status: public
title: Development Framework for Context-Aware Augmented Reality Applications
type: conference
user_id: '8447'
year: '2020'
...
---
_id: '48847'
abstract:
- lang: eng
text: Dynamic optimization problems have gained significant attention in evolutionary
computation as evolutionary algorithms (EAs) can easily adapt to changing environments.
We show that EAs can solve the graph coloring problem for bipartite graphs more
efficiently by using dynamic optimization. In our approach the graph instance
is given incrementally such that the EA can reoptimize its coloring when a new
edge introduces a conflict. We show that, when edges are inserted in a way that
preserves graph connectivity, Randomized Local Search (RLS) efficiently finds
a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS
and other EAs need exponential expected time in a static optimization scenario.
We investigate different ways of building up the graph by popular graph traversals
such as breadth-first-search and depth-first-search and analyse the resulting
runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$})
RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using
3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite
graph, outperforming offspring populations. This is the first example where an
island model guarantees a speedup that is not bounded in the number of islands.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics
for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’20. Association for Computing
Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.
Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.
https://doi.org/10.1145/3377930.3390174
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA},
series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization}, DOI={10.1145/3377930.3390174},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery,
2020. https://doi.org/10.1145/3377930.3390174.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized
Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285,
doi: 10.1145/3377930.3390174.'
mla: Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285,
doi:10.1145/3377930.3390174.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1277–1285.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:41Z
department:
- _id: '819'
doi: 10.1145/3377930.3390174
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1277–1285
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: More Effective Randomized Search Heuristics for Graph Coloring through Dynamic
Optimization
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48849'
abstract:
- lang: eng
text: One-shot optimization tasks require to determine the set of solution candidates
prior to their evaluation, i.e., without possibility for adaptive sampling. We
consider two variants, classic one-shot optimization (where our aim is to find
at least one solution of high quality) and one-shot regression (where the goal
is to fit a model that resembles the true problem as well as possible). For both
tasks it seems intuitive that well-distributed samples should perform better than
uniform or grid-based samples, since they show a better coverage of the decision
space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy
point sets are indeed very commonly used designs for one-shot optimization tasks.
We study in this work how well low star discrepancy correlates with performance
in one-shot optimization. Our results confirm an advantage of low-discrepancy
designs, but also indicate the correlation between discrepancy values and overall
performance is rather weak. We then demonstrate that commonly used designs may
be far from optimal. More precisely, we evolve 24 very specific designs that each
achieve good performance on one of our benchmark problems. Interestingly, we find
that these specifically designed samples yield surprisingly good performance across
the whole benchmark set. Our results therefore give strong indication that significant
performance gains over state-of-the-art one-shot sampling techniques are possible,
and that evolutionary algorithms can be an efficient means to evolve these.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies
for One-Shot Optimization Tasks. In: Parallel Problem Solving from Nature (PPSN
XVI). Springer-Verlag; 2020:111–124. doi:10.1007/978-3-030-58112-1_8'
apa: Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020).
Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem
Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin,
Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks},
DOI={10.1007/978-3-030-58112-1_8},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta
and Neumann, Frank}, year={2020}, pages={111–124} }'
chicago: 'Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank
Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In Parallel
Problem Solving from Nature (PPSN XVI), 111–124. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_8.'
ieee: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling
Strategies for One-Shot Optimization Tasks,” in Parallel Problem Solving from
Nature (PPSN XVI), 2020, pp. 111–124, doi: 10.1007/978-3-030-58112-1_8.'
mla: Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization
Tasks.” Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag,
2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8.
short: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem
Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
111–124.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:53Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_8
extern: '1'
keyword:
- Continuous optimization
- Fully parallel search
- One-shot optimization
- Regression
- Surrogate-assisted optimization
language:
- iso: eng
page: 111–124
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: Evolving Sampling Strategies for One-Shot Optimization Tasks
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48851'
abstract:
- lang: eng
text: Several important optimization problems in the area of vehicle routing can
be seen as variants of the classical Traveling Salesperson Problem (TSP). In the
area of evolutionary computation, the Traveling Thief Problem (TTP) has gained
increasing interest over the last 5 years. In this paper, we investigate the effect
of weights on such problems, in the sense that the cost of traveling increases
with respect to the weights of nodes already visited during a tour. This provides
abstractions of important TSP variants such as the Traveling Thief Problem and
time dependent TSP variants, and allows to study precisely the increase in difficulty
caused by weight dependence. We provide a 3.59-approximation for this weight dependent
version of TSP with metric distances and bounded positive weights. Furthermore,
we conduct experimental investigations for simple randomized local search with
classical mutation operators and two variants of the state-of-the-art evolutionary
algorithm EAX adapted to the weighted TSP. Our results show the impact of the
node weights on the position of the nodes in the resulting tour.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Katrin
full_name: Casel, Katrin
last_name: Casel
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling
Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243'
apa: 'Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight
Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized
Search Heuristics. Proceedings of the Genetic and Evolutionary Computation
Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243'
bibtex: '@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY,
USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson
Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={10.1145/3377930.3390243},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel,
Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294},
collection={GECCO ’20} }'
chicago: 'Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The
Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 1286–1294. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390243.'
ieee: 'J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent
Traveling Salesperson Problem: Approximation Algorithms and Randomized Search
Heuristics,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 1286–1294, doi: 10.1145/3377930.3390243.'
mla: 'Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem:
Approximation Algorithms and Randomized Search Heuristics.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.'
short: 'J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1286–1294.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:33Z
department:
- _id: '819'
doi: 10.1145/3377930.3390243
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1286–1294
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48845'
abstract:
- lang: eng
text: In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems
(VRPs) often imply repeated decision making on dynamic customer requests. As in
classical VRPs, tours have to be planned short while the number of serviced customers
has to be maximized at the same time resulting in a multi-objective problem. Beyond
that, however, dynamic requests lead to the need for re-planning of not yet realized
tour parts, while already realized tour parts are irreversible. In this paper
we study this type of bi-objective dynamic VRP including sequential decision making
and concurrent realization of decisions. We adopt a recently proposed Dynamic
Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend
it to the more realistic (here considered) scenario of multiple vehicles. We empirically
show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant
variant of the proposed DEMOA as well as with the dynamic single-vehicle approach
proposed earlier.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple
Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146'
apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing
of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation
Conference, 166–174. https://doi.org/10.1145/3377930.3390146
bibtex: '@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA},
series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles},
DOI={10.1145/3377930.3390146},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective
Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 166–174. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390146.'
ieee: 'J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of
Multiple Vehicles,” in Proceedings of the Genetic and Evolutionary Computation
Conference, 2020, pp. 166–174, doi: 10.1145/3377930.3390146.'
mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2020, pp. 166–174, doi:10.1145/3377930.3390146.
short: 'J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 166–174.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:24Z
department:
- _id: '819'
doi: 10.1145/3377930.3390146
extern: '1'
keyword:
- decision making
- dynamic optimization
- evolutionary algorithms
- multi-objective optimization
- vehicle routing
language:
- iso: eng
page: 166–174
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: Dynamic Bi-Objective Routing of Multiple Vehicles
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48844'
abstract:
- lang: eng
text: The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known
NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers
LKH and EAX and respective (restart) variants manage to calculate close-to optimal
or even optimal solutions, also for large instances with several thousand nodes
in reasonable time. In this work we extend existing benchmarking studies by addressing
anytime behaviour of inexact TSP solvers based on empirical runtime distributions
leading to an increased understanding of solver behaviour and the respective relation
to problem hardness. It turns out that performance ranking of solvers is highly
dependent on the focused approximation quality. Insights on intersection points
of performances offer huge potential for the construction of hybridized solvers
depending on instance features. Moreover, instance features tailored to anytime
performance and corresponding performance indicators will highly improve automated
algorithm selection models by including comprehensive information on solver quality.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers
and Perspectives for Automated Algorithm Selection. In: 2020 IEEE Congress
on Evolutionary Computation (CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185613'
apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact
TSP Solvers and Perspectives for Automated Algorithm Selection. 2020 IEEE Congress
on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185613
bibtex: '@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, United Kingdom},
title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated
Algorithm Selection}, DOI={10.1109/CEC48606.2020.9185613},
booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE
Press}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020},
pages={1–8} }'
chicago: 'Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior
of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In
2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, United
Kingdom: IEEE Press, 2020. https://doi.org/10.1109/CEC48606.2020.9185613.'
ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP
Solvers and Perspectives for Automated Algorithm Selection,” in 2020 IEEE Congress
on Evolutionary Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185613.'
mla: Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives
for Automated Algorithm Selection.” 2020 IEEE Congress on Evolutionary Computation
(CEC), IEEE Press, 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185613.
short: 'J. Bossek, P. Kerschke, H. Trautmann, in: 2020 IEEE Congress on Evolutionary
Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp. 1–8.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:16Z
department:
- _id: '819'
doi: 10.1109/CEC48606.2020.9185613
extern: '1'
language:
- iso: eng
page: 1–8
place: Glasgow, United Kingdom
publication: 2020 IEEE Congress on Evolutionary Computation (CEC)
publication_status: published
publisher: IEEE Press
status: public
title: Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm
Selection
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48850'
abstract:
- lang: eng
text: Sequential model-based optimization (SMBO) approaches are algorithms for solving
problems that require computationally or otherwise expensive function evaluations.
The key design principle of SMBO is a substitution of the true objective function
by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO
algorithms are intrinsically modular, leaving the user with many important design
choices. Significant research efforts go into understanding which settings perform
best for which type of problems. Most works, however, focus on the choice of the
model, the acquisition function, and the strategy used to optimize the latter.
The choice of the initial sampling strategy, however, receives much less attention.
Not surprisingly, quite diverging recommendations can be found in the literature.
We analyze in this work how the size and the distribution of the initial sample
influences the overall quality of the efficient global optimization (EGO) algorithm,
a well-known SMBO approach. While, overall, small initial budgets using Halton
sampling seem preferable, we also observe that the performance landscape is rather
unstructured. We furthermore identify several situations in which EGO performs
unfavorably against random sampling. Both observations indicate that an adaptive
SMBO design could be beneficial, making SMBO an interesting test-bed for automated
algorithm design.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects
on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:10.1145/3377930.3390155'
apa: 'Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. https://doi.org/10.1145/3377930.3390155'
bibtex: '@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO
’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based
Optimization: An Exploratory Case Study Based on BBOB}, DOI={10.1145/3377930.3390155},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr,
Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies
and Their Effects on Sequential Model-Based Optimization: An Exploratory Case
Study Based on BBOB.” In Proceedings of the Genetic and Evolutionary Computation
Conference, 778–786. GECCO ’20. New York, NY, USA: Association for Computing
Machinery, 2020. https://doi.org/10.1145/3377930.3390155.'
ieee: 'J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their
Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based
on BBOB,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 778–786, doi: 10.1145/3377930.3390155.'
mla: 'Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential
Model-Based Optimization: An Exploratory Case Study Based on BBOB.” Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 778–786, doi:10.1145/3377930.3390155.'
short: 'J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, New York, NY, USA,
2020, pp. 778–786.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:01Z
department:
- _id: '819'
doi: 10.1145/3377930.3390155
extern: '1'
keyword:
- continuous black-box optimization
- design of experiments
- initial design
- sequential model-based optimization
language:
- iso: eng
page: 778–786
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'Initial Design Strategies and Their Effects on Sequential Model-Based Optimization:
An Exploratory Case Study Based on BBOB'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48852'
abstract:
- lang: eng
text: The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial
optimisation problems. However, many real-world problems are composed of several
interacting components. The Traveling Thief Problem (TTP) addresses such interactions
by combining two combinatorial optimisation problems, namely the TSP and the Knapsack
Problem (KP). Recently, a new problem called the node weight dependent Traveling
Salesperson Problem (W-TSP) has been introduced where nodes have weights that
influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate
the structure of the optimised tours for W-TSP and TTP and the impact of using
each others fitness function. Our experimental results suggest (1) that the W-TSP
often can be solved better using the TTP fitness function and (2) final W-TSP
and TTP solutions show different distributions when compared with optimal TSP
or weighted greedy solutions.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Neumann A, Neumann F. Optimising Tours for the Weighted Traveling
Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of
Solutions. In: Parallel Problem Solving from Nature (PPSN XVI). Springer-Verlag;
2020:346–359. doi:10.1007/978-3-030-58112-1_24'
apa: 'Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the
Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural
Comparison of Solutions. Parallel Problem Solving from Nature (PPSN XVI),
346–359. https://doi.org/10.1007/978-3-030-58112-1_24'
bibtex: '@inproceedings{Bossek_Neumann_Neumann_2020, place={Berlin, Heidelberg},
title={Optimising Tours for the Weighted Traveling Salesperson Problem and the
Traveling Thief Problem: A Structural Comparison of Solutions}, DOI={10.1007/978-3-030-58112-1_24},
booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={346–359}
}'
chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Optimising Tours for
the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A
Structural Comparison of Solutions.” In Parallel Problem Solving from Nature
(PPSN XVI), 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. https://doi.org/10.1007/978-3-030-58112-1_24.'
ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted
Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison
of Solutions,” in Parallel Problem Solving from Nature (PPSN XVI), 2020,
pp. 346–359, doi: 10.1007/978-3-030-58112-1_24.'
mla: 'Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson
Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.”
Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, 2020,
pp. 346–359, doi:10.1007/978-3-030-58112-1_24.'
short: 'J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature
(PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:54Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_24
extern: '1'
keyword:
- Evolutionary algorithms
- Node weight dependent TSP
- Traveling Thief Problem
language:
- iso: eng
page: 346–359
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: 'Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling
Thief Problem: A Structural Comparison of Solutions'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48846'
abstract:
- lang: eng
text: We consider a dynamic bi-objective vehicle routing problem, where a subset
of customers ask for service over time. Therein, the distance traveled by a single
vehicle and the number of unserved dynamic requests is minimized by a dynamic
evolutionary multi-objective algorithm (DEMOA), which operates on discrete time
windows (eras). A decision is made at each era by a decision-maker, thus any decision
depends on irreversible decisions made in foregoing eras. To understand effects
of sequences of decision-making and interactions/dependencies between decisions
made, we conduct a series of experiments. More precisely, we fix a set of decision-maker
preferences D and the number of eras n{$<$}inf{$>$}t{$<$}/inf{$>$} and analyze
all $|D|\^{n_t}$ combinations of decision-maker options. We find that for random
uniform instances (a) the final selected solutions mainly depend on the final
decision and not on the decision history, (b) solutions are quite robust with
respect to the number of unvisited dynamic customers, and (c) solutions of the
dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In
contrast, for instances with clustered customers, we observe a strong dependency
on decision-making history as well as more variance in solution diversity.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic
Bi-Objective Vehicle Routing. In: 2020 IEEE Congress on Evolutionary Computation
(CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185778'
apa: Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision
Support in Dynamic Bi-Objective Vehicle Routing. 2020 IEEE Congress on Evolutionary
Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
bibtex: '@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, United
Kingdom}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing},
DOI={10.1109/CEC48606.2020.9185778},
booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE
Press}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann,
Heike}, year={2020}, pages={1–8} }'
chicago: 'Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann.
“Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In 2020
IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, United Kingdom:
IEEE Press, 2020. https://doi.org/10.1109/CEC48606.2020.9185778.'
ieee: 'J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support
in Dynamic Bi-Objective Vehicle Routing,” in 2020 IEEE Congress on Evolutionary
Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185778.'
mla: Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle
Routing.” 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press,
2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778.
short: 'J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: 2020 IEEE Congress on
Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp.
1–8.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:17Z
department:
- _id: '819'
doi: 10.1109/CEC48606.2020.9185778
extern: '1'
language:
- iso: eng
page: 1–8
place: Glasgow, United Kingdom
publication: 2020 IEEE Congress on Evolutionary Computation (CEC)
publication_status: published
publisher: IEEE Press
status: public
title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48879'
abstract:
- lang: eng
text: Evolving diverse sets of high quality solutions has gained increasing interest
in the evolutionary computation literature in recent years. With this paper, we
contribute to this area of research by examining evolutionary diversity optimisation
approaches for the classical Traveling Salesperson Problem (TSP). We study the
impact of using different diversity measures for a given set of tours and the
ability of evolutionary algorithms to obtain a diverse set of high quality solutions
when adopting these measures. Our studies show that a large variety of diverse
high quality tours can be achieved by using our approaches. Furthermore, we compare
our approaches in terms of theoretical properties and the final set of tours obtained
by the evolutionary diversity optimisation algorithm.
author:
- first_name: Anh Viet
full_name: Do, Anh Viet
last_name: Do
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for
the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689.
doi:10.1145/3377930.3389844'
apa: Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse
Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic
and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844
bibtex: '@inproceedings{Do_Bossek_Neumann_Neumann_2020, place={New York, NY, USA},
series={GECCO’20}, title={Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem}, DOI={10.1145/3377930.3389844},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Do, Anh Viet and Bossek,
Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={681–689}, collection={GECCO’20}
}'
chicago: 'Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving
Diverse Sets of Tours for the Travelling Salesperson Problem.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 681–689. GECCO’20.
New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3389844.'
ieee: 'A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of
Tours for the Travelling Salesperson Problem,” in Proceedings of the Genetic
and Evolutionary Computation Conference, 2020, pp. 681–689, doi: 10.1145/3377930.3389844.'
mla: Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson
Problem.” Proceedings of the Genetic and Evolutionary Computation Conference,
Association for Computing Machinery, 2020, pp. 681–689, doi:10.1145/3377930.3389844.
short: 'A.V. Do, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 681–689.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:48:50Z
department:
- _id: '819'
doi: 10.1145/3377930.3389844
extern: '1'
keyword:
- diversity maximisation
- evolutionary algorithms
- travelling salesperson problem
language:
- iso: eng
page: 681–689
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publisher: Association for Computing Machinery
series_title: GECCO’20
status: public
title: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48895'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) are general-purpose problem solvers that usually
perform an unbiased search. This is reasonable and desirable in a black-box scenario.
For combinatorial optimization problems, often more knowledge about the structure
of optimal solutions is given, which can be leveraged by means of biased search
operators. We consider the Minimum Spanning Tree (MST) problem in a single- and
multi-objective version, and introduce a biased mutation, which puts more emphasis
on the selection of edges of low rank in terms of low domination number. We present
example graphs where the biased mutation can significantly speed up the expected
runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate
that bias can lead to exponential runtime if "heavy" edges are necessarily part
of an optimal solution. However, on general graphs in the single-objective setting,
we show that a combined mutation operator which decides for unbiased or biased
edge selection in each step with equal probability exhibits a polynomial upper
bound - as unbiased mutation - in the worst case and benefits from bias if the
circumstances are favorable.
author:
- first_name: Vahid
full_name: Roostapour, Vahid
last_name: Roostapour
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms
with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In:
Proceedings of the 2020 Genetic and Evolutionary Computation Conference.
{GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168'
apa: Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
551–559. https://doi.org/10.1145/3377930.3390168
bibtex: '@inproceedings{Roostapour_Bossek_Neumann_2020, place={New York, NY, USA},
series={{GECCO} ’20}, title={Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}, DOI={10.1145/3377930.3390168}, booktitle={Proceedings
of the 2020 Genetic and Evolutionary Computation Conference}, publisher={Association
for Computing Machinery}, author={Roostapour, Vahid and Bossek, Jakob and Neumann,
Frank}, year={2020}, pages={551–559}, collection={{GECCO} ’20} }'
chicago: 'Roostapour, Vahid, Jakob Bossek, and Frank Neumann. “Runtime Analysis
of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum
Spanning Tree Problem.” In Proceedings of the 2020 Genetic and Evolutionary
Computation Conference, 551–559. {GECCO} ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390168.'
ieee: 'V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary
Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree
Problem,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference,
2020, pp. 551–559, doi: 10.1145/3377930.3390168.'
mla: Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with
Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” Proceedings
of the 2020 Genetic and Evolutionary Computation Conference, Association for
Computing Machinery, 2020, pp. 551–559, doi:10.1145/3377930.3390168.
short: 'V. Roostapour, J. Bossek, F. Neumann, in: Proceedings of the 2020 Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 551–559.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:38Z
department:
- _id: '819'
doi: 10.1145/3377930.3390168
extern: '1'
keyword:
- biased mutation
- evolutionary algorithms
- minimum spanning tree problem
- runtime analysis
language:
- iso: eng
page: 551–559
place: New York, NY, USA
publication: Proceedings of the 2020 Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publisher: Association for Computing Machinery
series_title: '{GECCO} ’20'
status: public
title: Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective
Minimum Spanning Tree Problem
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48897'
abstract:
- lang: eng
text: 'In this work we focus on the well-known Euclidean Traveling Salesperson Problem
(TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in
the context of per-instance algorithm selection (AS). We evolve instances with
nodes where the solvers show strongly different performance profiles. These instances
serve as a basis for an exploratory study on the identification of well-discriminating
problem characteristics (features). Our results in a nutshell: we show that even
though (1) promising features exist, (2) these are in line with previous results
from the literature, and (3) models trained with these features are more accurate
than models adopting sophisticated feature selection methods, the advantage is
not close to the virtual best solver in terms of penalized average runtime and
so is the performance gain over the single best solver. However, we show that
a feature-free deep neural network based approach solely based on visual representation
of the instances already matches classical AS model results and thus shows huge
potential for future studies.'
author:
- first_name: Moritz
full_name: Seiler, Moritz
last_name: Seiler
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive
Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson
Problem. In: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag;
2020:48–64. doi:10.1007/978-3-030-58112-1_4'
apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection
on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature}
(PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
bibtex: '@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin,
Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem}, DOI={10.1007/978-3-030-58112-1_4},
booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag},
author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal
and Trautmann, Heike}, year={2020}, pages={48–64} }'
chicago: 'Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike
Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem.” In Parallel Problem
Solving from {Nature} (PPSN XVI), 48–64. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_4.'
ieee: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning
as a Competitive Feature-Free Approach for Automated Algorithm Selection on the
Traveling Salesperson Problem,” in Parallel Problem Solving from {Nature} (PPSN
XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.'
mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach
for Automated Algorithm Selection on the Traveling Salesperson Problem.” Parallel
Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, 2020, pp. 48–64,
doi:10.1007/978-3-030-58112-1_4.
short: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem
Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
48–64.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:45Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_4
extern: '1'
keyword:
- Automated algorithm selection
- Deep learning
- Feature-based approaches
- Traveling Salesperson Problem
language:
- iso: eng
page: 48–64
place: Berlin, Heidelberg
publication: Parallel Problem Solving from {Nature} (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publisher: Springer-Verlag
status: public
title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm
Selection on the Traveling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...