---
_id: '46337'
abstract:
- lang: eng
text: A multiobjective perspective onto common performance measures such as the
PAR10 score or the expected runtime of single-objective stochastic solvers is
presented by directly investigating the tradeoff between the fraction of failed
runs and the average runtime. Multi-objective indicators operating in the bi-objective
space allow for an overall performance comparison on a set of instances paving
the way for instance-based automated algorithm selection techniques.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos
P, eds. Learning and Intelligent Optimization. Vol 11353. Lecture Notes
in Computer Science. Springer; 2019:215–219.'
apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I.
Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization
(Vol. 11353, pp. 215–219). Springer.'
bibtex: '@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes
in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent
Optimization}, publisher={Springer}, author={Bossek, Jakob and Trautmann, Heike},
editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019},
pages={215–219}, collection={Lecture Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent
Optimization, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos,
11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.'
ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization,
2019, vol. 11353, pp. 215–219.'
mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent
Optimization, edited by R Battiti et al., vol. 11353, Springer, 2019, pp.
215–219.'
short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos
(Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.'
date_created: 2023-08-04T07:44:10Z
date_updated: 2024-06-10T12:00:23Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Battiti, R
last_name: Battiti
- first_name: M
full_name: Brunato, M
last_name: Brunato
- first_name: I
full_name: Kotsireas, I
last_name: Kotsireas
- first_name: P
full_name: Pardalos, P
last_name: Pardalos
intvolume: ' 11353'
language:
- iso: eng
page: 215–219
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-05347-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected
Running Time'
type: conference
user_id: '15504'
volume: 11353
year: '2019'
...
---
_id: '46350'
abstract:
- lang: eng
text: 'The ubiquity of WiFi access points and the sharp increase in WiFi-enabled
devices carried by humans have paved the way for WiFi-based indoor positioning
and location analysis. Locating people in indoor environments has numerous applications
in robotics, crowd control, indoor facility optimization, and automated environment
mapping. However, existing WiFi-based positioning systems suffer from two major
problems: (1) their accuracy and precision is limited due to inherent noise induced
by indoor obstacles, and (2) they only occasionally provide location estimates,
namely when a WiFi-equipped device emits a signal. To mitigate these two issues,
we propose a novel Gaussian process (GP) model for WiFi signal strength measurements.
It allows for simultaneous smoothing (increasing accuracy and precision of estimators)
and interpolation (enabling continuous sampling of location estimates). Furthermore,
simple and efficient smoothing methods for location estimates are introduced to
improve localization performance in real-time settings. Experiments are conducted
on two data sets from a large real-world commercial indoor retail environment.
Results demonstrate that our approach provides significant improvements in terms
of precision and accuracy with respect to unfiltered data. Ultimately, the GP
model realizes continuous location sampling with consistently high quality location
estimates.'
author:
- first_name: J.E.
full_name: van Engelen, J.E.
last_name: van Engelen
- first_name: J.J.
full_name: van Lier, J.J.
last_name: van Lier
- first_name: F.W.
full_name: Takes, F.W.
last_name: Takes
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'van Engelen JE, van Lier JJ, Takes FW, Trautmann H. Accurate WiFi based indoor
positioning with continuous location sampling. In: Proceedings of the European
Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Database (ECML/PKDD). Springer; 2018:524–540.'
apa: van Engelen, J. E., van Lier, J. J., Takes, F. W., & Trautmann, H. (2018).
Accurate WiFi based indoor positioning with continuous location sampling. Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 524–540.
bibtex: '@inproceedings{van Engelen_van Lier_Takes_Trautmann_2018, place={Dublin,
Ireland}, title={Accurate WiFi based indoor positioning with continuous location
sampling}, booktitle={Proceedings of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}, publisher={Springer},
author={van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike},
year={2018}, pages={524–540} }'
chicago: 'Engelen, J.E. van, J.J. van Lier, F.W. Takes, and Heike Trautmann. “Accurate
WiFi Based Indoor Positioning with Continuous Location Sampling.” In Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 524–540. Dublin, Ireland: Springer,
2018.'
ieee: J. E. van Engelen, J. J. van Lier, F. W. Takes, and H. Trautmann, “Accurate
WiFi based indoor positioning with continuous location sampling,” in Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 2018, pp. 524–540.
mla: van Engelen, J. E., et al. “Accurate WiFi Based Indoor Positioning with Continuous
Location Sampling.” Proceedings of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD),
Springer, 2018, pp. 524–540.
short: 'J.E. van Engelen, J.J. van Lier, F.W. Takes, H. Trautmann, in: Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), Springer, Dublin, Ireland, 2018,
pp. 524–540.'
date_created: 2023-08-04T07:54:43Z
date_updated: 2023-10-16T13:33:18Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 524–540
place: Dublin, Ireland
publication: Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Database (ECML/PKDD)
publisher: Springer
status: public
title: Accurate WiFi based indoor positioning with continuous location sampling
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '46351'
abstract:
- lang: eng
text: Clustering is an important field in data mining that aims to reveal hidden
patterns in data sets. It is widely popular in marketing or medical applications
and used to identify groups of similar objects. Clustering possibly unbounded
and evolving data streams is of particular interest due to the widespread deployment
of large and fast data sources such as sensors. The vast majority of stream clustering
algorithms employ a two-phase approach where the stream is first summarized in
an online phase. Upon request, an offline phase reclusters the aggregations into
the final clusters. In this setup, the online component will idle and wait for
the next observation in times where the stream is slow. This paper proposes a
new stream clustering algorithm called evoStream which performs evolutionary optimization
in the idle times of the online phase to incrementally build and refine the final
clusters. Since the online phase would idle otherwise, our approach does not reduce
the processing speed while effectively removing the computational overhead of
the offline phase. In extensive experiments on real data streams we show that
the proposed algorithm allows to output clusters of high quality at any time within
the stream without the need for additional computational resources.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Carnein M, Trautmann H. evoStream — Evolutionary Stream Clustering Utilizing
Idle Times. Big Data Research. 2018;14:101–111. doi:10.1016/j.bdr.2018.05.005
apa: Carnein, M., & Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering
Utilizing Idle Times. Big Data Research, 14, 101–111. https://doi.org/10.1016/j.bdr.2018.05.005
bibtex: '@article{Carnein_Trautmann_2018, title={evoStream — Evolutionary Stream
Clustering Utilizing Idle Times}, volume={14}, DOI={10.1016/j.bdr.2018.05.005},
journal={Big Data Research}, author={Carnein, Matthias and Trautmann, Heike},
year={2018}, pages={101–111} }'
chicago: 'Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream
Clustering Utilizing Idle Times.” Big Data Research 14 (2018): 101–111.
https://doi.org/10.1016/j.bdr.2018.05.005.'
ieee: 'M. Carnein and H. Trautmann, “evoStream — Evolutionary Stream Clustering
Utilizing Idle Times,” Big Data Research, vol. 14, pp. 101–111, 2018, doi:
10.1016/j.bdr.2018.05.005.'
mla: Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering
Utilizing Idle Times.” Big Data Research, vol. 14, 2018, pp. 101–111, doi:10.1016/j.bdr.2018.05.005.
short: M. Carnein, H. Trautmann, Big Data Research 14 (2018) 101–111.
date_created: 2023-08-04T07:55:33Z
date_updated: 2023-10-16T13:33:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.bdr.2018.05.005
intvolume: ' 14'
language:
- iso: eng
page: 101–111
publication: Big Data Research
status: public
title: evoStream — Evolutionary Stream Clustering Utilizing Idle Times
type: journal_article
user_id: '15504'
volume: 14
year: '2018'
...
---
_id: '46353'
abstract:
- lang: eng
text: 'Incorporating decision makers'' preferences is of great significance in multiobjective
optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs),
aiming at a well-distributed subset of Pareto optimal solutions within the user-provided
region(s), are extensively investigated in this paper. An empirical comparison
is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA.
Experimental results show that T-SMS-EMOA has the best overall performance regarding
the hypervolume indicator within the target region, while T-NSGA-II is the fastest
algorithm. We also compare TMOEAs with other state-of-the-art preference-based
approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages
of TMOEAs. A case study in the mission planning of earth observation satellite
is carried out to verify the capabilities of TMOEAs in the real-world application.
Experimental results indicate that preferences can improve the searching ability
of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by
the decision maker.'
author:
- first_name: L
full_name: Li, L
last_name: Li
- first_name: Y
full_name: Wang, Y
last_name: Wang
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: N
full_name: Jing, N
last_name: Jing
- first_name: M
full_name: Emmerich, M
last_name: Emmerich
citation:
ama: Li L, Wang Y, Trautmann H, Jing N, Emmerich M. Multiobjective evolutionary
algorithms based on target region preferences. Swarm and Evolutionary Computation.
2018;40:196–215. doi:10.1016/j.swevo.2018.02.006
apa: Li, L., Wang, Y., Trautmann, H., Jing, N., & Emmerich, M. (2018). Multiobjective
evolutionary algorithms based on target region preferences. Swarm and Evolutionary
Computation, 40, 196–215. https://doi.org/10.1016/j.swevo.2018.02.006
bibtex: '@article{Li_Wang_Trautmann_Jing_Emmerich_2018, title={Multiobjective evolutionary
algorithms based on target region preferences}, volume={40}, DOI={10.1016/j.swevo.2018.02.006},
journal={Swarm and Evolutionary Computation}, author={Li, L and Wang, Y and Trautmann,
Heike and Jing, N and Emmerich, M}, year={2018}, pages={196–215} }'
chicago: 'Li, L, Y Wang, Heike Trautmann, N Jing, and M Emmerich. “Multiobjective
Evolutionary Algorithms Based on Target Region Preferences.” Swarm and Evolutionary
Computation 40 (2018): 196–215. https://doi.org/10.1016/j.swevo.2018.02.006.'
ieee: 'L. Li, Y. Wang, H. Trautmann, N. Jing, and M. Emmerich, “Multiobjective evolutionary
algorithms based on target region preferences,” Swarm and Evolutionary Computation,
vol. 40, pp. 196–215, 2018, doi: 10.1016/j.swevo.2018.02.006.'
mla: Li, L., et al. “Multiobjective Evolutionary Algorithms Based on Target Region
Preferences.” Swarm and Evolutionary Computation, vol. 40, 2018, pp. 196–215,
doi:10.1016/j.swevo.2018.02.006.
short: L. Li, Y. Wang, H. Trautmann, N. Jing, M. Emmerich, Swarm and Evolutionary
Computation 40 (2018) 196–215.
date_created: 2023-08-04T07:56:57Z
date_updated: 2023-10-16T13:34:21Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.swevo.2018.02.006
intvolume: ' 40'
language:
- iso: eng
page: 196–215
publication: Swarm and Evolutionary Computation
status: public
title: Multiobjective evolutionary algorithms based on target region preferences
type: journal_article
user_id: '15504'
volume: 40
year: '2018'
...
---
_id: '46348'
abstract:
- lang: eng
text: We analyze the effects of including local search techniques into a multi-objective
evolutionary algorithm for solving a bi-objective orienteering problem with a
single vehicle while the two conflicting objectives are minimization of travel
time and maximization of the number of visited customer locations. Experiments
are based on a large set of specifically designed problem instances with different
characteristics and it is shown that local search techniques focusing on one of
the objectives only improve the performance of the evolutionary algorithm in terms
of both objectives. The analysis also shows that local search techniques are capable
of sending locally optimal solutions to foremost fronts of the multi-objective
optimization process, and that these solutions then become the leading factors
of the evolutionary process.
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: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Guenter
full_name: Rudolph, Guenter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects
in Bi-Objective Orienteering. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’18. ACM; 2018:585–592. doi:10.1145/3205455.3205548'
apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018).
Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic
and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New
York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective
Orienteering}, DOI={10.1145/3205455.3205548},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan
and Rudolph, Guenter and Trautmann, Heike}, year={2018}, pages={585–592}, collection={GECCO
’18} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Guenter Rudolph, and
Heike Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 585–592. GECCO ’18.
New York, NY, USA: ACM, 2018. https://doi.org/10.1145/3205455.3205548.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search
Effects in Bi-Objective Orienteering,” in Proceedings of the Genetic and Evolutionary
Computation Conference, 2018, pp. 585–592, doi: 10.1145/3205455.3205548.'
mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”
Proceedings of the Genetic and Evolutionary Computation Conference, ACM,
2018, pp. 585–592, doi:10.1145/3205455.3205548.
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings
of the Genetic and Evolutionary Computation Conference, ACM, New York, NY, USA,
2018, pp. 585–592.'
date_created: 2023-08-04T07:53:16Z
date_updated: 2024-06-10T11:59:09Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3205455.3205548
language:
- iso: eng
page: 585–592
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-5618-3
publisher: ACM
series_title: GECCO ’18
status: public
title: Local Search Effects in Bi-Objective Orienteering
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '46352'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large
set of well-known benchmark instances and demonstrate complementary performance,
in that different instances may be solved most effectively by different algorithms.
We leverage this complementarity to build an algorithm selector, which selects
the best TSP solver on a per-instance basis and thus achieves significantly improved
performance compared to the single best solver, representing an advance in the
state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors
provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation (ECJ).
2018;26(4):597–620. doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation (ECJ), 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike},
year={2018}, pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation (ECJ) 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation
(ECJ), vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation (ECJ), vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation (ECJ) 26 (2018) 597–620.
date_created: 2023-08-04T07:56:15Z
date_updated: 2024-06-10T11:58:38Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation (ECJ)
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '15504'
volume: 26
year: '2018'
...
---
_id: '46349'
abstract:
- lang: eng
text: Performance comparisons of optimization algorithms are heavily influenced
by the underlying indicator(s). In this paper we investigate commonly used performance
indicators for single-objective stochastic solvers, such as the Penalized Average
Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark
performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a
methodology for analyzing the effects of (usually heuristically set) indicator
parametrizations - such as the penalty factor and the method used for aggregating
across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion.
; 2018:1737–1744. doi:10.1145/3205651.3208233'
apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of
State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP
Solvers. Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={Kyoto, Japan}, title={Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers}, DOI={10.1145/3205651.3208233},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann,
Heike}, year={2018}, pages={1737–1744} }'
chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion, 1737–1744. Kyoto, Japan, 2018. https://doi.org/10.1145/3205651.3208233.'
ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art
Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion,
2018, pp. 1737–1744, doi: 10.1145/3205651.3208233.'
mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion,
2018, pp. 1737–1744, doi:10.1145/3205651.3208233.'
short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO ’18) Companion, Kyoto, Japan, 2018,
pp. 1737–1744.'
date_created: 2023-08-04T07:53:59Z
date_updated: 2024-06-10T11:58:54Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3205651.3208233
language:
- iso: eng
page: 1737–1744
place: Kyoto, Japan
publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’18) Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7/18/07
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '46355'
abstract:
- lang: eng
text: "In this chapter we present the adaptions of the recently proposed Directed
Search method to the context of unconstrained parameter dependent multi-objective
optimization problems (PMOPs). The new method, called \U0001D706-DS, is capable
of performing a movement both toward and along the solution set of a given differentiable
PMOP. We first discuss the basic variants of the method that use gradient information
and describe subsequently modifications that allow for a gradient free realization.
Finally, we show that \U0001D706-DS can be used to understand the behavior of
stochastic local search within PMOPs to a certain extent which might be interesting
for the development of future local search engines, or evolutionary strategies,
for the treatment of such problems. We underline all our statements with several
numerical results indicating the strength of the novel approach."
author:
- first_name: Sosa Hernández V
full_name: Adrián, Sosa Hernández V
last_name: Adrián
- first_name: A
full_name: Lara, A
last_name: Lara
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
citation:
ama: 'Adrián SHV, Lara A, Trautmann H, Rudolph G, Schütze O. The Directed Search
Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems.
In: Schütze O, Trujillo L, Legrand P, Maldonado Y, eds. NEO 15. Springer
International Publishing; 2017:281–330. doi:10.1007/978-3-319-44003-3_12'
apa: Adrián, S. H. V., Lara, A., Trautmann, H., Rudolph, G., & Schütze, O. (2017).
The Directed Search Method for Unconstrained Parameter Dependent Multi-objective
Optimization Problems. In O. Schütze, L. Trujillo, P. Legrand, & Y. Maldonado
(Eds.), NEO 15 (pp. 281–330). Springer International Publishing. https://doi.org/10.1007/978-3-319-44003-3_12
bibtex: '@inbook{Adrián_Lara_Trautmann_Rudolph_Schütze_2017, place={Cham}, title={The
Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization
Problems}, DOI={10.1007/978-3-319-44003-3_12},
booktitle={NEO 15}, publisher={Springer International Publishing}, author={Adrián,
Sosa Hernández V and Lara, A and Trautmann, Heike and Rudolph, G and Schütze,
O}, editor={Schütze, O and Trujillo, L and Legrand, P and Maldonado, Y}, year={2017},
pages={281–330} }'
chicago: 'Adrián, Sosa Hernández V, A Lara, Heike Trautmann, G Rudolph, and O Schütze.
“The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective
Optimization Problems.” In NEO 15, edited by O Schütze, L Trujillo, P Legrand,
and Y Maldonado, 281–330. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-44003-3_12.'
ieee: 'S. H. V. Adrián, A. Lara, H. Trautmann, G. Rudolph, and O. Schütze, “The
Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization
Problems,” in NEO 15, O. Schütze, L. Trujillo, P. Legrand, and Y. Maldonado,
Eds. Cham: Springer International Publishing, 2017, pp. 281–330.'
mla: Adrián, Sosa Hernández V., et al. “The Directed Search Method for Unconstrained
Parameter Dependent Multi-Objective Optimization Problems.” NEO 15, edited
by O Schütze et al., Springer International Publishing, 2017, pp. 281–330, doi:10.1007/978-3-319-44003-3_12.
short: 'S.H.V. Adrián, A. Lara, H. Trautmann, G. Rudolph, O. Schütze, in: O. Schütze,
L. Trujillo, P. Legrand, Y. Maldonado (Eds.), NEO 15, Springer International Publishing,
Cham, 2017, pp. 281–330.'
date_created: 2023-08-04T15:01:27Z
date_updated: 2023-10-16T13:34:49Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-44003-3_12
editor:
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: L
full_name: Trujillo, L
last_name: Trujillo
- first_name: P
full_name: Legrand, P
last_name: Legrand
- first_name: Y
full_name: Maldonado, Y
last_name: Maldonado
language:
- iso: eng
page: 281–330
place: Cham
publication: NEO 15
publication_identifier:
isbn:
- 978-3-319-44003-3
publisher: Springer International Publishing
status: public
title: The Directed Search Method for Unconstrained Parameter Dependent Multi-objective
Optimization Problems
type: book_chapter
user_id: '15504'
year: '2017'
...
---
_id: '46360'
abstract:
- lang: eng
text: Nowadays customers expect a seamless interaction with companies throughout
all available communication channels. However, many companies rely on different
software solutions to handle each channel, which leads to heterogeneous IT infrastructures
and isolated data sources. Omni-Channel CRM is a holistic approach towards a unified
view on the customer across all channels. This paper introduces three case studies
which demonstrate challenges of omni-channel CRM and the value it can provide.
The first case study shows how to integrate and visualise data from different
sources which can support operational and strategic decision. In the second case
study, a social media analysis approach is discussed which provides benefits by
offering reports of service performance across channels. The third case study
applies customer segmentation to an online fashion retailer in order to identify
customer profiles.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Markus
full_name: Heuchert, Markus
last_name: Heuchert
- first_name: Leschek
full_name: Homann, Leschek
last_name: Homann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Gottfried
full_name: Vossen, Gottfried
last_name: Vossen
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
- first_name: Karsten
full_name: Kraume, Karsten
last_name: Kraume
citation:
ama: 'Carnein M, Heuchert M, Homann L, et al. Towards Efficient and Informative
Omni-Channel Customer Relationship Management. In: de Cesare S, Ulrich F, eds.
Proceedings of the 36$^th$ International Conference on Conceptual Modeling
(ER’17). Vol 10651. Lecture Notes in Computer Science. Springer International
Publishing; 2017:69–78. doi:10.1007/978-3-319-70625-2_7'
apa: Carnein, M., Heuchert, M., Homann, L., Trautmann, H., Vossen, G., Becker, J.,
& Kraume, K. (2017). Towards Efficient and Informative Omni-Channel Customer
Relationship Management. In S. de Cesare & F. Ulrich (Eds.), Proceedings
of the 36$^th$ International Conference on Conceptual Modeling (ER’17) (Vol.
10651, pp. 69–78). Springer International Publishing. https://doi.org/10.1007/978-3-319-70625-2_7
bibtex: '@inproceedings{Carnein_Heuchert_Homann_Trautmann_Vossen_Becker_Kraume_2017,
place={Valencia, Spain}, series={Lecture Notes in Computer Science}, title={Towards
Efficient and Informative Omni-Channel Customer Relationship Management}, volume={10651},
DOI={10.1007/978-3-319-70625-2_7},
booktitle={Proceedings of the 36$^th$ International Conference on Conceptual Modeling
(ER’17)}, publisher={Springer International Publishing}, author={Carnein, Matthias
and Heuchert, Markus and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried
and Becker, Jörg and Kraume, Karsten}, editor={de Cesare, Sergio and Ulrich, Frank},
year={2017}, pages={69–78}, collection={Lecture Notes in Computer Science} }'
chicago: 'Carnein, Matthias, Markus Heuchert, Leschek Homann, Heike Trautmann, Gottfried
Vossen, Jörg Becker, and Karsten Kraume. “Towards Efficient and Informative Omni-Channel
Customer Relationship Management.” In Proceedings of the 36$^th$ International
Conference on Conceptual Modeling (ER’17), edited by Sergio de Cesare and
Frank Ulrich, 10651:69–78. Lecture Notes in Computer Science. Valencia, Spain:
Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-70625-2_7.'
ieee: 'M. Carnein et al., “Towards Efficient and Informative Omni-Channel
Customer Relationship Management,” in Proceedings of the 36$^th$ International
Conference on Conceptual Modeling (ER’17), 2017, vol. 10651, pp. 69–78, doi:
10.1007/978-3-319-70625-2_7.'
mla: Carnein, Matthias, et al. “Towards Efficient and Informative Omni-Channel Customer
Relationship Management.” Proceedings of the 36$^th$ International Conference
on Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich,
vol. 10651, Springer International Publishing, 2017, pp. 69–78, doi:10.1007/978-3-319-70625-2_7.
short: 'M. Carnein, M. Heuchert, L. Homann, H. Trautmann, G. Vossen, J. Becker,
K. Kraume, in: S. de Cesare, F. Ulrich (Eds.), Proceedings of the 36$^th$ International
Conference on Conceptual Modeling (ER’17), Springer International Publishing,
Valencia, Spain, 2017, pp. 69–78.'
date_created: 2023-08-04T15:05:43Z
date_updated: 2023-10-16T13:36:40Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-70625-2_7
editor:
- first_name: Sergio
full_name: de Cesare, Sergio
last_name: de Cesare
- first_name: Frank
full_name: Ulrich, Frank
last_name: Ulrich
intvolume: ' 10651'
language:
- iso: eng
page: 69–78
place: Valencia, Spain
publication: Proceedings of the 36$^th$ International Conference on Conceptual Modeling
(ER’17)
publication_identifier:
isbn:
- 978-3-319-70625-2
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Towards Efficient and Informative Omni-Channel Customer Relationship Management
type: conference
user_id: '15504'
volume: 10651
year: '2017'
...
---
_id: '46361'
abstract:
- lang: eng
text: Until recently, customer service was exclusively provided over traditional
channels. Cus- tomers could write an email or call a service center if they had
questions or problems with a product or service. In recent times, this has changed
dramatically as companies explore new channels to offer customer service. With
the increasing popularity of social media, more companies thrive to provide customer
service also over Facebook and Twitter. Companies aim to provide a better customer
ex- perience by offering more convenient channels to contact a company. In addition,
this unburdens traditional channels which are costly to maintain. This paper empirically
evaluates the performance of customer service in social media by analysing a multitude
of companies in the airline industry. We have collected several million customer
service requests from Twitter and Facebook and auto- matically analyzed how efficient
the service strategies of the respective companies are in terms of response rate
and time.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Leschek
full_name: Homann, Leschek
last_name: Homann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Gottfried
full_name: Vossen, Gottfried
last_name: Vossen
- first_name: Karsten
full_name: Kraume, Karsten
last_name: Kraume
citation:
ama: 'Carnein M, Homann L, Trautmann H, Vossen G, Kraume K. Customer Service in
Social Media — An Empirical Study of the Airline Industry. In: Ritter N, Schwarz
H, Klettke M, Thor A, Kopp O, Bernhard MW, eds. Proceedings of the 17$^th$
Conference on Database Systems for Business, Technology, and Web (BTW ’17).
Vol P-266. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2017:33–40.'
apa: 'Carnein, M., Homann, L., Trautmann, H., Vossen, G., & Kraume, K. (2017).
Customer Service in Social Media — An Empirical Study of the Airline Industry.
In N. Ritter, H. Schwarz, M. Klettke, A. Thor, O. Kopp, & M. W. Bernhard (Eds.),
Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology,
and Web (BTW ’17): Vol. P-266 (pp. 33–40). Gesellschaft für Informatik.'
bibtex: '@inproceedings{Carnein_Homann_Trautmann_Vossen_Kraume_2017, place={Stuttgart,
Germany}, series={Lecture Notes in Informatics (LNI)}, title={Customer Service
in Social Media — An Empirical Study of the Airline Industry}, volume={P-266},
booktitle={Proceedings of the 17$^th$ Conference on Database Systems for Business,
Technology, and Web (BTW ’17)}, publisher={Gesellschaft für Informatik}, author={Carnein,
Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Kraume,
Karsten}, editor={Ritter, Norbert and Schwarz, Holger and Klettke, Meike and Thor,
Andreas and Kopp, Oliver and Bernhard, Matthias Wieland}, year={2017}, pages={33–40},
collection={Lecture Notes in Informatics (LNI)} }'
chicago: 'Carnein, Matthias, Leschek Homann, Heike Trautmann, Gottfried Vossen,
and Karsten Kraume. “Customer Service in Social Media — An Empirical Study of
the Airline Industry.” In Proceedings of the 17$^th$ Conference on Database
Systems for Business, Technology, and Web (BTW ’17), edited by Norbert Ritter,
Holger Schwarz, Meike Klettke, Andreas Thor, Oliver Kopp, and Matthias Wieland
Bernhard, P-266:33–40. Lecture Notes in Informatics (LNI). Stuttgart, Germany:
Gesellschaft für Informatik, 2017.'
ieee: M. Carnein, L. Homann, H. Trautmann, G. Vossen, and K. Kraume, “Customer Service
in Social Media — An Empirical Study of the Airline Industry,” in Proceedings
of the 17$^th$ Conference on Database Systems for Business, Technology, and Web
(BTW ’17), 2017, vol. P-266, pp. 33–40.
mla: Carnein, Matthias, et al. “Customer Service in Social Media — An Empirical
Study of the Airline Industry.” Proceedings of the 17$^th$ Conference on Database
Systems for Business, Technology, and Web (BTW ’17), edited by Norbert Ritter
et al., vol. P-266, Gesellschaft für Informatik, 2017, pp. 33–40.
short: 'M. Carnein, L. Homann, H. Trautmann, G. Vossen, K. Kraume, in: N. Ritter,
H. Schwarz, M. Klettke, A. Thor, O. Kopp, M.W. Bernhard (Eds.), Proceedings of
the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW
’17), Gesellschaft für Informatik, Stuttgart, Germany, 2017, pp. 33–40.'
date_created: 2023-08-04T15:06:41Z
date_updated: 2023-10-16T13:36:58Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Norbert
full_name: Ritter, Norbert
last_name: Ritter
- first_name: Holger
full_name: Schwarz, Holger
last_name: Schwarz
- first_name: Meike
full_name: Klettke, Meike
last_name: Klettke
- first_name: Andreas
full_name: Thor, Andreas
last_name: Thor
- first_name: Oliver
full_name: Kopp, Oliver
last_name: Kopp
- first_name: Matthias Wieland
full_name: Bernhard, Matthias Wieland
last_name: Bernhard
language:
- iso: eng
page: 33–40
place: Stuttgart, Germany
publication: Proceedings of the 17$^th$ Conference on Database Systems for Business,
Technology, and Web (BTW ’17)
publication_identifier:
issn:
- 978-3-88579-660-2
publisher: Gesellschaft für Informatik
series_title: Lecture Notes in Informatics (LNI)
status: public
title: Customer Service in Social Media — An Empirical Study of the Airline Industry
type: conference
user_id: '15504'
volume: P-266
year: '2017'
...
---
_id: '46356'
abstract:
- lang: eng
text: Integrating user preferences in Evolutionary Multiobjective Optimization (EMO)
is currently a prevalent research topic. There is a large variety of preference
handling methods (originated from Multicriteria decision making, MCDM) and EMO
methods, which have been combined in various ways. This paper proposes a Web Ontology
Language (OWL) ontology to model and systematize the knowledge of preference-based
multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on
how to build and use the ontology with the help of Protégé. Different use-cases,
including training new learners, querying and reasoning are exemplified and show
remarkable benefit for both EMO and MCDM communities.
author:
- first_name: L
full_name: Li, L
last_name: Li
- first_name: I
full_name: Yevseyeva, I
last_name: Yevseyeva
- first_name: V
full_name: Basto-Fernandes, V
last_name: Basto-Fernandes
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: N
full_name: Jing, N
last_name: Jing
- first_name: M
full_name: Emmerich, M
last_name: Emmerich
citation:
ama: 'Li L, Yevseyeva I, Basto-Fernandes V, Trautmann H, Jing N, Emmerich M. Building
and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms.
In: Trautmann H, Rudolph G, Klamroth K, et al., eds. Evolutionary Multi-Criterion
Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March
19-22, 2017, Proceedings. Springer International Publishing; 2017:406–421.
doi:10.1007/978-3-319-54157-0_28'
apa: 'Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., &
Emmerich, M. (2017). Building and Using an Ontology of Preference-Based Multiobjective
Evolutionary Algorithms. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze,
M. Wiecek, Y. Jin, & C. Grimme (Eds.), Evolutionary Multi-Criterion Optimization:
9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017,
Proceedings (pp. 406–421). Springer International Publishing. https://doi.org/10.1007/978-3-319-54157-0_28'
bibtex: '@inbook{Li_Yevseyeva_Basto-Fernandes_Trautmann_Jing_Emmerich_2017, place={Cham},
title={Building and Using an Ontology of Preference-Based Multiobjective Evolutionary
Algorithms}, DOI={10.1007/978-3-319-54157-0_28},
booktitle={Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference,
EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}, publisher={Springer
International Publishing}, author={Li, L and Yevseyeva, I and Basto-Fernandes,
V and Trautmann, Heike and Jing, N and Emmerich, M}, editor={Trautmann, H and
Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme,
C}, year={2017}, pages={406–421} }'
chicago: 'Li, L, I Yevseyeva, V Basto-Fernandes, Heike Trautmann, N Jing, and M
Emmerich. “Building and Using an Ontology of Preference-Based Multiobjective Evolutionary
Algorithms.” In Evolutionary Multi-Criterion Optimization: 9$^th$ International
Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited
by H Trautmann, G Rudolph, K Klamroth, O Schütze, M Wiecek, Y Jin, and C Grimme,
406–421. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-54157-0_28.'
ieee: 'L. Li, I. Yevseyeva, V. Basto-Fernandes, H. Trautmann, N. Jing, and M. Emmerich,
“Building and Using an Ontology of Preference-Based Multiobjective Evolutionary
Algorithms,” in Evolutionary Multi-Criterion Optimization: 9$^th$ International
Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, H.
Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme,
Eds. Cham: Springer International Publishing, 2017, pp. 406–421.'
mla: 'Li, L., et al. “Building and Using an Ontology of Preference-Based Multiobjective
Evolutionary Algorithms.” Evolutionary Multi-Criterion Optimization: 9$^th$
International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings,
edited by H Trautmann et al., Springer International Publishing, 2017, pp. 406–421,
doi:10.1007/978-3-319-54157-0_28.'
short: 'L. Li, I. Yevseyeva, V. Basto-Fernandes, H. Trautmann, N. Jing, M. Emmerich,
in: H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme
(Eds.), Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference,
EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, Springer International
Publishing, Cham, 2017, pp. 406–421.'
date_created: 2023-08-04T15:02:20Z
date_updated: 2023-10-16T13:35:17Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-54157-0_28
editor:
- first_name: H
full_name: Trautmann, H
last_name: Trautmann
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: K
full_name: Klamroth, K
last_name: Klamroth
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: M
full_name: Wiecek, M
last_name: Wiecek
- first_name: Y
full_name: Jin, Y
last_name: Jin
- first_name: C
full_name: Grimme, C
last_name: Grimme
language:
- iso: eng
page: 406–421
place: Cham
publication: 'Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference,
EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings'
publication_identifier:
isbn:
- 978-3-319-54157-0
publisher: Springer International Publishing
status: public
title: Building and Using an Ontology of Preference-Based Multiobjective Evolutionary
Algorithms
type: book_chapter
user_id: '15504'
year: '2017'
...
---
_id: '46357'
abstract:
- lang: eng
text: The liner shipping fleet repositioning problem (LSFRP) is a central optimization
problem within the container shipping industry. Several approaches exist for solving
this problem using exact and heuristic techniques, however all of them use a single
objective function for determining an optimal solution. We propose a multi-objective
approach based on a simulated annealing heuristic so that repositioning coordinators
can better balance profit making with cost-savings and environmental sustainability.
As the first multi-objective approach in the area of liner shipping routing, we
show that giving more options to decision makers need not be costly. Indeed, our
approach requires no extra runtime than a weighted objective heuristic and provides
a rich set of solutions along the Pareto front.
author:
- first_name: K
full_name: Tierney, K
last_name: Tierney
- first_name: J
full_name: Handali, J
last_name: Handali
- first_name: C
full_name: Grimme, C
last_name: Grimme
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Tierney K, Handali J, Grimme C, Trautmann H. Multi-objective Optimization
for Liner Shipping Fleet Repositioning. In: Trautmann H, Rudolph G, Klamroth K,
et al., eds. Evolutionary Multi-Criterion Optimization: 9$^th$ International
Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Springer
International Publishing; 2017:622–638. doi:10.1007/978-3-319-54157-0_42'
apa: 'Tierney, K., Handali, J., Grimme, C., & Trautmann, H. (2017). Multi-objective
Optimization for Liner Shipping Fleet Repositioning. In H. Trautmann, G. Rudolph,
K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, & C. Grimme (Eds.), Evolutionary
Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster,
Germany, March 19-22, 2017, Proceedings (pp. 622–638). Springer International
Publishing. https://doi.org/10.1007/978-3-319-54157-0_42'
bibtex: '@inbook{Tierney_Handali_Grimme_Trautmann_2017, place={Cham}, title={Multi-objective
Optimization for Liner Shipping Fleet Repositioning}, DOI={10.1007/978-3-319-54157-0_42},
booktitle={Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference,
EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}, publisher={Springer
International Publishing}, author={Tierney, K and Handali, J and Grimme, C and
Trautmann, Heike}, editor={Trautmann, H and Rudolph, G and Klamroth, K and Schütze,
O and Wiecek, M and Jin, Y and Grimme, C}, year={2017}, pages={622–638} }'
chicago: 'Tierney, K, J Handali, C Grimme, and Heike Trautmann. “Multi-Objective
Optimization for Liner Shipping Fleet Repositioning.” In Evolutionary Multi-Criterion
Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March
19-22, 2017, Proceedings, edited by H Trautmann, G Rudolph, K Klamroth, O
Schütze, M Wiecek, Y Jin, and C Grimme, 622–638. Cham: Springer International
Publishing, 2017. https://doi.org/10.1007/978-3-319-54157-0_42.'
ieee: 'K. Tierney, J. Handali, C. Grimme, and H. Trautmann, “Multi-objective Optimization
for Liner Shipping Fleet Repositioning,” in Evolutionary Multi-Criterion Optimization:
9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017,
Proceedings, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek,
Y. Jin, and C. Grimme, Eds. Cham: Springer International Publishing, 2017, pp.
622–638.'
mla: 'Tierney, K., et al. “Multi-Objective Optimization for Liner Shipping Fleet
Repositioning.” Evolutionary Multi-Criterion Optimization: 9$^th$ International
Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited
by H Trautmann et al., Springer International Publishing, 2017, pp. 622–638, doi:10.1007/978-3-319-54157-0_42.'
short: 'K. Tierney, J. Handali, C. Grimme, H. Trautmann, in: H. Trautmann, G. Rudolph,
K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (Eds.), Evolutionary Multi-Criterion
Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March
19-22, 2017, Proceedings, Springer International Publishing, Cham, 2017, pp. 622–638.'
date_created: 2023-08-04T15:03:17Z
date_updated: 2023-10-16T13:35:41Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-54157-0_42
editor:
- first_name: H
full_name: Trautmann, H
last_name: Trautmann
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: K
full_name: Klamroth, K
last_name: Klamroth
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: M
full_name: Wiecek, M
last_name: Wiecek
- first_name: Y
full_name: Jin, Y
last_name: Jin
- first_name: C
full_name: Grimme, C
last_name: Grimme
language:
- iso: eng
page: 622–638
place: Cham
publication: 'Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference,
EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings'
publication_identifier:
isbn:
- 978-3-319-54157-0
publisher: Springer International Publishing
status: public
title: Multi-objective Optimization for Liner Shipping Fleet Repositioning
type: book_chapter
user_id: '15504'
year: '2017'
...
---
_id: '46359'
abstract:
- lang: eng
text: This paper proposes a new stream clustering algorithm for text streams. The
algorithm combines concepts from stream clustering and text analysis in order
to incrementally maintain a number of text droplets that represent topics within
the stream. Our algorithm adapts to changes of topic over time and can handle
noise and outliers gracefully by decaying the importance of irrelevant clusters.
We demonstrate the performance of our approach by using more than one million
real-world texts from the video streaming platform Twitch.tv.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Carnein M, Assenmacher D, Trautmann H. Stream Clustering of Chat Messages
with Applications to Twitch Streams. In: de Cesare S, Ulrich F, eds. Proceedings
of the 36$^th$ International Conference on Conceptual Modeling (ER’17). Springer
International Publishing; 2017:79–88. doi:10.1007/978-3-319-70625-2_8'
apa: Carnein, M., Assenmacher, D., & Trautmann, H. (2017). Stream Clustering
of Chat Messages with Applications to Twitch Streams. In S. de Cesare & F.
Ulrich (Eds.), Proceedings of the 36$^th$ International Conference on Conceptual
Modeling (ER’17) (pp. 79–88). Springer International Publishing. https://doi.org/10.1007/978-3-319-70625-2_8
bibtex: '@inproceedings{Carnein_Assenmacher_Trautmann_2017, place={Valencia, Spain},
title={Stream Clustering of Chat Messages with Applications to Twitch Streams},
DOI={10.1007/978-3-319-70625-2_8},
booktitle={Proceedings of the 36$^th$ International Conference on Conceptual Modeling
(ER’17)}, publisher={Springer International Publishing}, author={Carnein, Matthias
and Assenmacher, Dennis and Trautmann, Heike}, editor={de Cesare, Sergio and Ulrich,
Frank}, year={2017}, pages={79–88} }'
chicago: 'Carnein, Matthias, Dennis Assenmacher, and Heike Trautmann. “Stream Clustering
of Chat Messages with Applications to Twitch Streams.” In Proceedings of the
36$^th$ International Conference on Conceptual Modeling (ER’17), edited by
Sergio de Cesare and Frank Ulrich, 79–88. Valencia, Spain: Springer International
Publishing, 2017. https://doi.org/10.1007/978-3-319-70625-2_8.'
ieee: 'M. Carnein, D. Assenmacher, and H. Trautmann, “Stream Clustering of Chat
Messages with Applications to Twitch Streams,” in Proceedings of the 36$^th$
International Conference on Conceptual Modeling (ER’17), 2017, pp. 79–88,
doi: 10.1007/978-3-319-70625-2_8.'
mla: Carnein, Matthias, et al. “Stream Clustering of Chat Messages with Applications
to Twitch Streams.” Proceedings of the 36$^th$ International Conference on
Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich,
Springer International Publishing, 2017, pp. 79–88, doi:10.1007/978-3-319-70625-2_8.
short: 'M. Carnein, D. Assenmacher, H. Trautmann, in: S. de Cesare, F. Ulrich (Eds.),
Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17),
Springer International Publishing, Valencia, Spain, 2017, pp. 79–88.'
date_created: 2023-08-04T15:04:57Z
date_updated: 2023-10-16T13:36:23Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-70625-2_8
editor:
- first_name: Sergio
full_name: de Cesare, Sergio
last_name: de Cesare
- first_name: Frank
full_name: Ulrich, Frank
last_name: Ulrich
language:
- iso: eng
page: 79–88
place: Valencia, Spain
publication: Proceedings of the 36$^th$ International Conference on Conceptual Modeling
(ER’17)
publication_identifier:
isbn:
- 978-3-319-70625-2
publisher: Springer International Publishing
status: public
title: Stream Clustering of Chat Messages with Applications to Twitch Streams
type: conference
user_id: '15504'
year: '2017'
...
---
_id: '46362'
abstract:
- lang: eng
text: Social bots are currently regarded an influential but also somewhat mysterious
factor in public discourse and opinion making. They are considered to be capable
of massively distributing propaganda in social and online media, and their application
is even suspected to be partly responsible for recent election results. Astonishingly,
the term social bot is not well defined and different scientific disciplines use
divergent definitions. This work starts with a balanced definition attempt, before
providing an overview of how social bots actually work (taking the example of
Twitter) and what their current technical limitations are. Despite recent research
progress in Deep Learning and Big Data, there are many activities bots cannot
handle well. We then discuss how bot capabilities can be extended and controlled
by integrating humans into the process and reason that this is currently the most
promising way to realize meaningful interactions with other humans. This finally
leads to the conclusion that hybridization is a challenge for current detection
mechanisms and has to be handled with more sophisticated approaches to identify
political propaganda distributed with social bots.
author:
- first_name: C
full_name: Grimme, C
last_name: Grimme
- first_name: M
full_name: Preuss, M
last_name: Preuss
- first_name: L
full_name: Adam, L
last_name: Adam
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Grimme C, Preuss M, Adam L, Trautmann H. Social Bots: Human-Like by Means
of Human Control? Big Data. 2017;5(4):279–293. doi:10.1089/big.2017.0044'
apa: 'Grimme, C., Preuss, M., Adam, L., & Trautmann, H. (2017). Social Bots:
Human-Like by Means of Human Control? Big Data, 5(4), 279–293. https://doi.org/10.1089/big.2017.0044'
bibtex: '@article{Grimme_Preuss_Adam_Trautmann_2017, title={Social Bots: Human-Like
by Means of Human Control?}, volume={5}, DOI={10.1089/big.2017.0044},
number={4}, journal={Big Data}, author={Grimme, C and Preuss, M and Adam, L and
Trautmann, Heike}, year={2017}, pages={279–293} }'
chicago: 'Grimme, C, M Preuss, L Adam, and Heike Trautmann. “Social Bots: Human-Like
by Means of Human Control?” Big Data 5, no. 4 (2017): 279–293. https://doi.org/10.1089/big.2017.0044.'
ieee: 'C. Grimme, M. Preuss, L. Adam, and H. Trautmann, “Social Bots: Human-Like
by Means of Human Control?,” Big Data, vol. 5, no. 4, pp. 279–293, 2017,
doi: 10.1089/big.2017.0044.'
mla: 'Grimme, C., et al. “Social Bots: Human-Like by Means of Human Control?” Big
Data, vol. 5, no. 4, 2017, pp. 279–293, doi:10.1089/big.2017.0044.'
short: C. Grimme, M. Preuss, L. Adam, H. Trautmann, Big Data 5 (2017) 279–293.
date_created: 2023-08-04T15:07:56Z
date_updated: 2023-10-16T13:37:14Z
department:
- _id: '34'
- _id: '819'
doi: 10.1089/big.2017.0044
intvolume: ' 5'
issue: '4'
language:
- iso: eng
page: 279–293
publication: Big Data
status: public
title: 'Social Bots: Human-Like by Means of Human Control?'
type: journal_article
user_id: '15504'
volume: 5
year: '2017'
...
---
_id: '46358'
abstract:
- lang: eng
text: Analysing streaming data has received considerable attention over the recent
years. A key research area in this field is stream clustering which aims to recognize
patterns in a possibly unbounded data stream of varying speed and structure. Over
the past decades a multitude of new stream clustering algorithms have been proposed.
However, to the best of our knowledge, no rigorous analysis and comparison of
the different approaches has been performed. Our paper fills this gap and provides
extensive experiments for a total of ten popular algorithms. We utilize a number
of standard data sets of both, real and synthetic data and identify key weaknesses
and strengths of the existing algorithms.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Carnein M, Assenmacher D, Trautmann H. An Empirical Comparison of Stream Clustering
Algorithms. In: Proceedings of the ACM International Conference on Computing
Frontiers (CF ’17). ; 2017:361–365. doi:10.1145/3075564.3078887'
apa: Carnein, M., Assenmacher, D., & Trautmann, H. (2017). An Empirical Comparison
of Stream Clustering Algorithms. Proceedings of the ACM International Conference
on Computing Frontiers (CF ’17), 361–365. https://doi.org/10.1145/3075564.3078887
bibtex: '@inproceedings{Carnein_Assenmacher_Trautmann_2017, place={Siena, Italy},
title={An Empirical Comparison of Stream Clustering Algorithms}, DOI={10.1145/3075564.3078887},
booktitle={Proceedings of the ACM International Conference on Computing Frontiers
(CF ’17)}, author={Carnein, Matthias and Assenmacher, Dennis and Trautmann, Heike},
year={2017}, pages={361–365} }'
chicago: Carnein, Matthias, Dennis Assenmacher, and Heike Trautmann. “An Empirical
Comparison of Stream Clustering Algorithms.” In Proceedings of the ACM International
Conference on Computing Frontiers (CF ’17), 361–365. Siena, Italy, 2017. https://doi.org/10.1145/3075564.3078887.
ieee: 'M. Carnein, D. Assenmacher, and H. Trautmann, “An Empirical Comparison of
Stream Clustering Algorithms,” in Proceedings of the ACM International Conference
on Computing Frontiers (CF ’17), 2017, pp. 361–365, doi: 10.1145/3075564.3078887.'
mla: Carnein, Matthias, et al. “An Empirical Comparison of Stream Clustering Algorithms.”
Proceedings of the ACM International Conference on Computing Frontiers (CF
’17), 2017, pp. 361–365, doi:10.1145/3075564.3078887.
short: 'M. Carnein, D. Assenmacher, H. Trautmann, in: Proceedings of the ACM International
Conference on Computing Frontiers (CF ’17), Siena, Italy, 2017, pp. 361–365.'
date_created: 2023-08-04T15:04:09Z
date_updated: 2023-10-16T13:35:59Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3075564.3078887
language:
- iso: eng
page: 361–365
place: Siena, Italy
publication: Proceedings of the ACM International Conference on Computing Frontiers
(CF ’17)
publication_identifier:
isbn:
- 978-1-4503-4487-6/17/05
status: public
title: An Empirical Comparison of Stream Clustering Algorithms
type: conference
user_id: '15504'
year: '2017'
...
---
_id: '46364'
abstract:
- lang: eng
text: Automated algorithm configuration procedures play an increasingly important
role in the development and application of algorithms for a wide range of computationally
challenging problems. Until very recently, these configuration procedures were
limited to optimising a single performance objective, such as the running time
or solution quality achieved by the algorithm being configured. However, in many
applications there is more than one performance objective of interest. This gives
rise to the multi-objective automatic algorithm configuration problem, which involves
finding a Pareto set of configurations of a given target algorithm that characterises
trade-offs between multiple performance objectives. In this work, we introduce
MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective
algorithm configuration framework ParamILS, and demonstrate that it produces good
results on several challenging bi-objective algorithm configuration scenarios
compared to a base-line obtained from using a state-of-the-art single-objective
algorithm configurator.
author:
- first_name: A
full_name: Blot, A
last_name: Blot
- first_name: H
full_name: Hoos, H
last_name: Hoos
- first_name: L
full_name: Jourdan, L
last_name: Jourdan
- first_name: M
full_name: Marmion, M
last_name: Marmion
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Blot A, Hoos H, Jourdan L, Marmion M, Trautmann H. MO-ParamILS: A Multi-objective
Automatic Algorithm Configuration Framework. In: et al. Joaquin V, ed. LION
2016: Learning and Intelligent Optimization. Vol 10079. LNTCS. Springer International
Publishing; 2016:32–47. doi:10.1007/978-3-319-50349-3_3'
apa: 'Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016).
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In
V. et al. Joaquin (Ed.), LION 2016: Learning and Intelligent Optimization
(Vol. 10079, pp. 32–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_3'
bibtex: '@inproceedings{Blot_Hoos_Jourdan_Marmion_Trautmann_2016, place={Cham},
series={LNTCS}, title={MO-ParamILS: A Multi-objective Automatic Algorithm Configuration
Framework}, volume={10079}, DOI={10.1007/978-3-319-50349-3_3},
booktitle={LION 2016: Learning and Intelligent Optimization}, publisher={Springer
International Publishing}, author={Blot, A and Hoos, H and Jourdan, L and Marmion,
M and Trautmann, Heike}, editor={et al. Joaquin, Vanschooren}, year={2016}, pages={32–47},
collection={LNTCS} }'
chicago: 'Blot, A, H Hoos, L Jourdan, M Marmion, and Heike Trautmann. “MO-ParamILS:
A Multi-Objective Automatic Algorithm Configuration Framework.” In LION 2016:
Learning and Intelligent Optimization, edited by Vanschooren et al. Joaquin,
10079:32–47. LNTCS. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_3.'
ieee: 'A. Blot, H. Hoos, L. Jourdan, M. Marmion, and H. Trautmann, “MO-ParamILS:
A Multi-objective Automatic Algorithm Configuration Framework,” in LION 2016:
Learning and Intelligent Optimization, 2016, vol. 10079, pp. 32–47, doi: 10.1007/978-3-319-50349-3_3.'
mla: 'Blot, A., et al. “MO-ParamILS: A Multi-Objective Automatic Algorithm Configuration
Framework.” LION 2016: Learning and Intelligent Optimization, edited by
Vanschooren et al. Joaquin, vol. 10079, Springer International Publishing, 2016,
pp. 32–47, doi:10.1007/978-3-319-50349-3_3.'
short: 'A. Blot, H. Hoos, L. Jourdan, M. Marmion, H. Trautmann, in: V. et al. Joaquin
(Ed.), LION 2016: Learning and Intelligent Optimization, Springer International
Publishing, Cham, 2016, pp. 32–47.'
date_created: 2023-08-04T15:10:09Z
date_updated: 2023-10-16T13:37:50Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-50349-3_3
editor:
- first_name: Vanschooren
full_name: et al. Joaquin, Vanschooren
last_name: et al. Joaquin
intvolume: ' 10079'
language:
- iso: eng
page: 32–47
place: Cham
publication: 'LION 2016: Learning and Intelligent Optimization'
publisher: Springer International Publishing
series_title: LNTCS
status: public
title: 'MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework'
type: conference
user_id: '15504'
volume: 10079
year: '2016'
...
---
_id: '46363'
abstract:
- lang: eng
text: "The averaged Hausdorff distance has been proposed as an indicator for assessing
the quality of finitely sized approximations of the Pareto front of a multiobjective
problem. Since many set-based, iterative optimization algorithms store their currently
best approximation in an internal archive these approximations are also termed
archives. In case of two objectives and continuous variables it is known that
the best approximations in terms of averaged Hausdorff distance are subsets of
the Pareto front if it is concave. If it is linear or circularly concave the points
of the best approximation are equally spaced.\r\n\r\nHere, it is proven that the
optimal averaged Hausdorff approximation and the Pareto front have an empty intersection
if the Pareto front is circularly convex. But the points of the best approximation
are equally spaced and they rapidly approach the Pareto front for increasing size
of the approximation."
author:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Schütze O, Trautmann H. On the Closest Averaged Hausdorff Archive
for a Circularly Convex Pareto Front. In: Squillero G, Burelli P, eds. Applications
of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016,
Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II. Springer
International Publishing; 2016:42–55. doi:10.1007/978-3-319-31153-1_4'
apa: 'Rudolph, G., Schütze, O., & Trautmann, H. (2016). On the Closest Averaged
Hausdorff Archive for a Circularly Convex Pareto Front. In G. Squillero &
P. Burelli (Eds.), Applications of Evolutionary Computation: 19$^th$ European
Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II (pp. 42–55). Springer International Publishing. https://doi.org/10.1007/978-3-319-31153-1_4'
bibtex: '@inbook{Rudolph_Schütze_Trautmann_2016, place={Cham}, title={On the Closest
Averaged Hausdorff Archive for a Circularly Convex Pareto Front}, DOI={10.1007/978-3-319-31153-1_4},
booktitle={Applications of Evolutionary Computation: 19$^th$ European Conference,
EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II}, publisher={Springer International Publishing}, author={Rudolph, G and
Schütze, O and Trautmann, Heike}, editor={Squillero, G and Burelli, P}, year={2016},
pages={42–55} }'
chicago: 'Rudolph, G, O Schütze, and Heike Trautmann. “On the Closest Averaged Hausdorff
Archive for a Circularly Convex Pareto Front.” In Applications of Evolutionary
Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal,
March 30 — April 1, 2016, Proceedings, Part II, edited by G Squillero and
P Burelli, 42–55. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-31153-1_4.'
ieee: 'G. Rudolph, O. Schütze, and H. Trautmann, “On the Closest Averaged Hausdorff
Archive for a Circularly Convex Pareto Front,” in Applications of Evolutionary
Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal,
March 30 — April 1, 2016, Proceedings, Part II, G. Squillero and P. Burelli,
Eds. Cham: Springer International Publishing, 2016, pp. 42–55.'
mla: 'Rudolph, G., et al. “On the Closest Averaged Hausdorff Archive for a Circularly
Convex Pareto Front.” Applications of Evolutionary Computation: 19$^th$ European
Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II, edited by G Squillero and P Burelli, Springer International Publishing,
2016, pp. 42–55, doi:10.1007/978-3-319-31153-1_4.'
short: 'G. Rudolph, O. Schütze, H. Trautmann, in: G. Squillero, P. Burelli (Eds.),
Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications
2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II, Springer
International Publishing, Cham, 2016, pp. 42–55.'
date_created: 2023-08-04T15:09:14Z
date_updated: 2023-10-16T13:37:33Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-31153-1_4
editor:
- first_name: G
full_name: Squillero, G
last_name: Squillero
- first_name: P
full_name: Burelli, P
last_name: Burelli
language:
- iso: eng
page: 42–55
place: Cham
publication: 'Applications of Evolutionary Computation: 19$^th$ European Conference,
EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part
II'
publication_identifier:
isbn:
- 978-3-319-31153-1
publisher: Springer International Publishing
status: public
title: On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front
type: book_chapter
user_id: '15504'
year: '2016'
...
---
_id: '46369'
abstract:
- lang: eng
text: This paper formally defines multimodality in multiobjective optimization (MO).
We introduce a test-bed in which multimodal MO problems with known properties
can be constructed as well as numerical characteristics of the resulting landscape.
Gradient- and local search based strategies are compared on exemplary problems
together with specific performance indicators in the multimodal MO setting. By
this means the foundation for Exploratory Landscape Analysis in MO is provided.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: André
full_name: Deutz, André
last_name: Deutz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
citation:
ama: 'Kerschke P, Wang H, Preuss M, et al. Towards Analyzing Multimodality of Multiobjective
Landscapes. In: Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV). Lecture Notes in Computer Science.
Springer; 2016:962–972. doi:10.1007/978-3-319-45823-6_90'
apa: Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., &
Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes.
Proceedings of the 14$^th$ International Conference on Parallel Problem Solving
from Nature (PPSN XIV), 962–972. https://doi.org/10.1007/978-3-319-45823-6_90
bibtex: '@inproceedings{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2016,
place={Edinburgh, Scotland}, series={Lecture Notes in Computer Science}, title={Towards
Analyzing Multimodality of Multiobjective Landscapes}, DOI={10.1007/978-3-319-45823-6_90},
booktitle={Proceedings of the 14$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XIV)}, publisher={Springer}, author={Kerschke, Pascal
and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann,
Heike and Emmerich, Michael}, year={2016}, pages={962–972}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz,
Heike Trautmann, and Michael Emmerich. “Towards Analyzing Multimodality of Multiobjective
Landscapes.” In Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), 962–972. Lecture Notes in Computer
Science. Edinburgh, Scotland: Springer, 2016. https://doi.org/10.1007/978-3-319-45823-6_90.'
ieee: 'P. Kerschke et al., “Towards Analyzing Multimodality of Multiobjective
Landscapes,” in Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), 2016, pp. 962–972, doi: 10.1007/978-3-319-45823-6_90.'
mla: Kerschke, Pascal, et al. “Towards Analyzing Multimodality of Multiobjective
Landscapes.” Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), Springer, 2016, pp. 962–972, doi:10.1007/978-3-319-45823-6_90.
short: 'P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich,
in: Proceedings of the 14$^th$ International Conference on Parallel Problem Solving
from Nature (PPSN XIV), Springer, Edinburgh, Scotland, 2016, pp. 962–972.'
date_created: 2023-08-04T15:16:02Z
date_updated: 2023-10-16T13:39:42Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-45823-6_90
language:
- iso: eng
page: 962–972
place: Edinburgh, Scotland
publication: Proceedings of the 14$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XIV)
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Towards Analyzing Multimodality of Multiobjective Landscapes
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '46367'
abstract:
- lang: eng
text: When selecting the best suited algorithm for an unknown optimization problem,
it is useful to possess some a priori knowledge of the problem at hand. In the
context of single-objective, continuous optimization problems such knowledge can
be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically
identifies properties of a landscape, e.g., the so-called funnel structures, based
on an initial sample. In this paper, we extract the relevant features (for detecting
funnels) out of a large set of landscape features when only given a small initial
sample consisting of 50 x D observations, where D is the number of decision space
dimensions. This is already in the range of the start population sizes of many
evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used
for training the classifier, and the approach is then very successfully validated
on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching
competition problems.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Simon
full_name: Wessing, Simon
last_name: Wessing
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Preuss M, Wessing S, Trautmann H. Low-Budget Exploratory Landscape
Analysis on Multiple Peaks Models. In: Proceedings of the 18$^th$ Annual Conference
on Genetic and Evolutionary Computation. ; 2016:229–236. doi:10.1145/2908812.2908845'
apa: Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget
Exploratory Landscape Analysis on Multiple Peaks Models. Proceedings of the
18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236.
https://doi.org/10.1145/2908812.2908845
bibtex: '@inproceedings{Kerschke_Preuss_Wessing_Trautmann_2016, place={Denver, CO,
USA}, title={Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models},
DOI={10.1145/2908812.2908845},
booktitle={Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary
Computation}, author={Kerschke, Pascal and Preuss, Mike and Wessing, Simon and
Trautmann, Heike}, year={2016}, pages={229–236} }'
chicago: Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. “Low-Budget
Exploratory Landscape Analysis on Multiple Peaks Models.” In Proceedings of
the 18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236.
Denver, CO, USA, 2016. https://doi.org/10.1145/2908812.2908845.
ieee: 'P. Kerschke, M. Preuss, S. Wessing, and H. Trautmann, “Low-Budget Exploratory
Landscape Analysis on Multiple Peaks Models,” in Proceedings of the 18$^th$
Annual Conference on Genetic and Evolutionary Computation, 2016, pp. 229–236,
doi: 10.1145/2908812.2908845.'
mla: Kerschke, Pascal, et al. “Low-Budget Exploratory Landscape Analysis on Multiple
Peaks Models.” Proceedings of the 18$^th$ Annual Conference on Genetic and
Evolutionary Computation, 2016, pp. 229–236, doi:10.1145/2908812.2908845.
short: 'P. Kerschke, M. Preuss, S. Wessing, H. Trautmann, in: Proceedings of the
18$^th$ Annual Conference on Genetic and Evolutionary Computation, Denver, CO,
USA, 2016, pp. 229–236.'
date_created: 2023-08-04T15:14:06Z
date_updated: 2023-10-16T13:38:47Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2908812.2908845
language:
- iso: eng
page: 229–236
place: Denver, CO, USA
publication: Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- 978-1-4503-4206-3
status: public
title: Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '46371'
abstract:
- lang: eng
text: "One main task in evolutionary multiobjective optimization (EMO) is to obtain
a suitable finite size approximation of the Pareto front which is the image of
the solution set, termed the Pareto set, of a given multiobjective optimization
problem. In the technical literature, the characteristic of the desired approximation
is commonly expressed by closeness to the Pareto front and a sufficient spread
of the solutions obtained. In this paper, we first make an effort to show by theoretical
and empirical findings that the recently proposed Averaged Hausdorff (or Δ\U0001D45D-)
indicator indeed aims at fulfilling both performance criteria for bi-objective
optimization problems. In the second part of this paper, standard EMO algorithms
combined with a specialized archiver and a postprocessing step based on the Δ\U0001D45D
indicator are introduced which sufficiently approximate the Δ\U0001D45D-optimal
archives and generate solutions evenly spread along the Pareto front."
author:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: C
full_name: Grimme, C
last_name: Grimme
- first_name: C
full_name: Domínguez-Medina, C
last_name: Domínguez-Medina
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Schütze O, Grimme C, Domínguez-Medina C, Trautmann H. Optimal averaged
Hausdorff archives for bi-objective problems: theoretical and numerical results.
Computational Optimization and Applications (Comput Optim Appl). 2016;64(2):589–618.
doi:10.1007/s10589-015-9815-8'
apa: 'Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann,
H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical
and numerical results. Computational Optimization and Applications (Comput.
Optim. Appl.), 64(2), 589–618. https://doi.org/10.1007/s10589-015-9815-8'
bibtex: '@article{Rudolph_Schütze_Grimme_Domínguez-Medina_Trautmann_2016, title={Optimal
averaged Hausdorff archives for bi-objective problems: theoretical and numerical
results}, volume={64}, DOI={10.1007/s10589-015-9815-8},
number={2}, journal={Computational Optimization and Applications (Comput. Optim.
Appl.)}, author={Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina,
C and Trautmann, Heike}, year={2016}, pages={589–618} }'
chicago: 'Rudolph, G, O Schütze, C Grimme, C Domínguez-Medina, and Heike Trautmann.
“Optimal Averaged Hausdorff Archives for Bi-Objective Problems: Theoretical and
Numerical Results.” Computational Optimization and Applications (Comput. Optim.
Appl.) 64, no. 2 (2016): 589–618. https://doi.org/10.1007/s10589-015-9815-8.'
ieee: 'G. Rudolph, O. Schütze, C. Grimme, C. Domínguez-Medina, and H. Trautmann,
“Optimal averaged Hausdorff archives for bi-objective problems: theoretical and
numerical results,” Computational Optimization and Applications (Comput. Optim.
Appl.), vol. 64, no. 2, pp. 589–618, 2016, doi: 10.1007/s10589-015-9815-8.'
mla: 'Rudolph, G., et al. “Optimal Averaged Hausdorff Archives for Bi-Objective
Problems: Theoretical and Numerical Results.” Computational Optimization and
Applications (Comput. Optim. Appl.), vol. 64, no. 2, 2016, pp. 589–618, doi:10.1007/s10589-015-9815-8.'
short: G. Rudolph, O. Schütze, C. Grimme, C. Domínguez-Medina, H. Trautmann, Computational
Optimization and Applications (Comput. Optim. Appl.) 64 (2016) 589–618.
date_created: 2023-08-04T15:17:48Z
date_updated: 2023-10-16T13:40:21Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/s10589-015-9815-8
intvolume: ' 64'
issue: '2'
language:
- iso: eng
page: 589–618
publication: Computational Optimization and Applications (Comput. Optim. Appl.)
status: public
title: 'Optimal averaged Hausdorff archives for bi-objective problems: theoretical
and numerical results'
type: journal_article
user_id: '15504'
volume: 64
year: '2016'
...