---
_id: '48877'
abstract:
- lang: eng
text: OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them online
to work and collaborate more efficiently. In this paper, we present an R package
to interface with the OpenML platform and illustrate its usage in combination
with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5,
2016). We show how the OpenML package allows R users to easily search, download
and upload data sets and machine learning tasks. Furthermore, we also show how
to upload results of experiments, share them with others and download results
from other users. Beyond ensuring reproducibility of results, the OpenML platform
automates much of the drudge work, speeds up research, facilitates collaboration
and increases the users’ visibility online.
author:
- first_name: Giuseppe
full_name: Casalicchio, Giuseppe
last_name: Casalicchio
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Michel
full_name: Lang, Michel
last_name: Lang
- first_name: Dominik
full_name: Kirchhoff, Dominik
last_name: Kirchhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Benjamin
full_name: Hofner, Benjamin
last_name: Hofner
- first_name: Heidi
full_name: Seibold, Heidi
last_name: Seibold
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
citation:
ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to
the Machine Learning Platform OpenML. Computational Statistics. 2019;34(3):977–991.
doi:10.1007/s00180-017-0742-2'
apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner,
B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package
to Connect to the Machine Learning Platform OpenML. Computational Statistics,
34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2'
bibtex: '@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019,
title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML},
volume={34}, DOI={10.1007/s00180-017-0742-2},
number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe
and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal
and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
year={2019}, pages={977–991} }'
chicago: 'Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal
Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl.
“OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” Computational
Statistics 34, no. 3 (2019): 977–991. https://doi.org/10.1007/s00180-017-0742-2.'
ieee: 'G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp.
977–991, 2019, doi: 10.1007/s00180-017-0742-2.'
mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML.” Computational Statistics, vol. 34, no. 3, 2019,
pp. 977–991, doi:10.1007/s00180-017-0742-2.'
short: G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner,
H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:51:17Z
department:
- _id: '819'
doi: 10.1007/s00180-017-0742-2
intvolume: ' 34'
issue: '3'
keyword:
- Databases
- Machine learning
- R
- Reproducible research
language:
- iso: eng
page: 977–991
publication: Computational Statistics
publication_identifier:
issn:
- 0943-4062
status: public
title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML'
type: journal_article
user_id: '102979'
volume: 34
year: '2019'
...
---
_id: '46339'
abstract:
- lang: eng
text: 'Evolutionary algorithms have successfully been applied to evolve problem
instances that exhibit a significant difference in performance for a given algorithm
or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP).
Creating a large variety of instances is crucial for successful applications in
the blooming field of algorithm selection. In this paper, we introduce new and
creative mutation operators for evolving instances of the TSP. We show that adopting
those operators in an evolutionary algorithm allows for the generation of benchmark
sets with highly desirable properties: (1) novelty by clear visual distinction
to established benchmark sets in the field, (2) visual and quantitative diversity
in the space of TSP problem characteristics, and (3) significant performance differences
with respect to the restart versions of heuristic state-of-the-art TSP solvers
EAX and LKH. The important aspect of diversity is addressed and achieved solely
by the proposed mutation operators and not enforced by explicit diversity preservation.'
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: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving
Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: Friedrich
T, Doerr C, Arnold D, eds. Proceedings of the 15$^th$ ACM/SIGEVO Workshop on
Foundations of Genetic Algorithms (FOGA XV). ; 2019:58–71. doi:10.1145/3299904.3340307'
apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann,
H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation
Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of
the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)
(pp. 58–71). https://doi.org/10.1145/3299904.3340307
bibtex: '@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={Potsdam,
Germany}, title={Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators}, DOI={10.1145/3299904.3340307},
booktitle={Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)}, author={Bossek, Jakob and Kerschke, Pascal and Neumann,
Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, editor={Friedrich,
Tobias and Doerr, Carola and Arnold, Dirk}, year={2019}, pages={58–71} }'
chicago: Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann,
and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators.” In Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations
of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich, Carola Doerr,
and Dirk Arnold, 58–71. Potsdam, Germany, 2019. https://doi.org/10.1145/3299904.3340307.
ieee: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann,
“Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,”
in Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV), 2019, pp. 58–71, doi: 10.1145/3299904.3340307.'
mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and
Creative Mutation Operators.” Proceedings of the 15$^th$ ACM/SIGEVO Workshop
on Foundations of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich
et al., 2019, pp. 58–71, doi:10.1145/3299904.3340307.
short: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann,
in: T. Friedrich, C. Doerr, D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO
Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 2019,
pp. 58–71.'
date_created: 2023-08-04T07:45:39Z
date_updated: 2024-06-10T11:59:26Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3299904.3340307
editor:
- first_name: Tobias
full_name: Friedrich, Tobias
last_name: Friedrich
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Dirk
full_name: Arnold, Dirk
last_name: Arnold
language:
- iso: eng
page: 58–71
place: Potsdam, Germany
publication: Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)
status: public
title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46338'
abstract:
- lang: eng
text: We tackle a bi-objective dynamic orienteering problem where customer requests
arise as time passes by. The goal is to minimize the tour length traveled by a
single delivery vehicle while simultaneously keeping the number of dismissed dynamic
customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm
which is grounded on insights gained from a previous series of work on an a-posteriori
version of the problem, where all request times are known in advance. In our experiments,
we simulate different decision maker strategies and evaluate the development of
the Pareto-front approximations on exemplary problem instances. It turns out,
that despite severely reduced computational budget and no oracle-knowledge of
request times the dynamic EMOA is capable of producing approximations which partially
dominate the results of the a-posteriori EMOA and dynamic integer linear programming
strategies.
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: Günter
full_name: Rudolph, Günter
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. Bi-Objective Orienteering:
Towards a Dynamic Multi-Objective Evolutionary Algorithm. In: Deb K, Goodman E,
Coello CCA, et al., eds. Evolutionary Multi-Criterion Optimization (EMO).
Vol 11411. Lecture Notes in Computer Science. Springer International Publishing;
2019:516–528. doi:10.1007/978-3-030-12598-1_41'
apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019).
Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.
In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim,
& P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol.
11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41'
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={East
Lansing, Michigan, USA}, series={Lecture Notes in Computer Science}, title={Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}, volume={11411},
DOI={10.1007/978-3-030-12598-1_41},
booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer
International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel,
Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and
Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen,
Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), edited
by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa
Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:516–528. Lecture Notes in
Computer Science. East Lansing, Michigan, USA: Springer International Publishing,
2019. https://doi.org/10.1007/978-3-030-12598-1_41.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm,” in Evolutionary
Multi-Criterion Optimization (EMO), 2019, vol. 11411, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.'
mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective
Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO),
edited by Kalyanmoy Deb et al., vol. 11411, Springer International Publishing,
2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.'
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E.
Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.),
Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing,
East Lansing, Michigan, USA, 2019, pp. 516–528.'
date_created: 2023-08-04T07:44:59Z
date_updated: 2024-06-10T12:00:05Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-12598-1_41
editor:
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Erik
full_name: Goodman, Erik
last_name: Goodman
- first_name: Coello Carlos A.
full_name: Coello, Coello Carlos A.
last_name: Coello
- first_name: Kathrin
full_name: Klamroth, Kathrin
last_name: Klamroth
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Patrick
full_name: Reed, Patrick
last_name: Reed
intvolume: ' 11411'
language:
- iso: eng
page: 516–528
place: East Lansing, Michigan, USA
publication: Evolutionary Multi-Criterion Optimization (EMO)
publication_identifier:
isbn:
- 978-3-030-12597-4
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm'
type: conference
user_id: '15504'
volume: 11411
year: '2019'
...
---
_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: '48839'
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: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
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. Association for Computing Machinery; 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={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, year={2018},
pages={585–592}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter 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: Association for Computing Machinery, 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, Association
for Computing Machinery, 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, Association for Computing
Machinery, New York, NY, USA, 2018, pp. 585–592.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:42:14Z
department:
- _id: '819'
doi: 10.1145/3205455.3205548
extern: '1'
keyword:
- combinatorial optimization
- metaheuristics
- multi-objective optimization
- orienteering
- transportation
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
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Local Search Effects in Bi-Objective Orienteering
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48867'
abstract:
- lang: eng
text: Assessing the performance of stochastic optimization algorithms in the field
of multi-objective optimization is of utmost importance. Besides the visual comparison
of the obtained approximation sets, more sophisticated methods have been proposed
in the last decade, e. g., a variety of quantitative performance indicators or
statistical tests. In this paper, we present tools implemented in the R package
ecr, which assist in performing comprehensive and sound comparison and evaluation
of multi-objective evolutionary algorithms following recommendations from the
literature.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. In: Proceedings of the Genetic and Evolutionary Computation
Conference Companion. GECCO ’18. Association for Computing Machinery; 2018:1350–1356.
doi:10.1145/3205651.3208312'
apa: Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
bibtex: '@inproceedings{Bossek_2018, place={New York, NY, USA}, series={GECCO ’18},
title={Performance Assessment of Multi-Objective Evolutionary Algorithms with
the R Package ecr}, DOI={10.1145/3205651.3208312},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob},
year={2018}, pages={1350–1356}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary
Algorithms with the R Package Ecr.” In Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 1350–1356. GECCO ’18. New York, NY, USA:
Association for Computing Machinery, 2018. https://doi.org/10.1145/3205651.3208312.'
ieee: 'J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr,” in Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 2018, pp. 1350–1356, doi: 10.1145/3205651.3208312.'
mla: Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package Ecr.” Proceedings of the Genetic and Evolutionary Computation
Conference Companion, Association for Computing Machinery, 2018, pp. 1350–1356,
doi:10.1145/3205651.3208312.
short: 'J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference
Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1350–1356.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:04Z
department:
- _id: '819'
doi: 10.1145/3205651.3208312
extern: '1'
keyword:
- evolutionary optimization
- performance assessment
- software-tools
language:
- iso: eng
page: 1350–1356
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Performance Assessment of Multi-Objective Evolutionary Algorithms with the
R Package ecr
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48885'
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
last_name: Trautmann
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 Companion. GECCO’18.
Association for Computing Machinery; 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
Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA},
series={GECCO’18}, 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
Companion}, publisher={Association for Computing Machinery}, author={Kerschke,
Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744},
collection={GECCO’18} }'
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
Companion, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing
Machinery, 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 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 Companion, Association
for Computing Machinery, 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 Companion, Association for Computing Machinery,
New York, NY, USA, 2018, pp. 1737–1744.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:38Z
department:
- _id: '819'
doi: 10.1145/3205651.3208233
extern: '1'
keyword:
- algorithm selection
- optimization
- performance measures
- transportation
- travelling salesperson problem
language:
- iso: eng
page: 1737–1744
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publisher: Association for Computing Machinery
series_title: GECCO’18
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48880'
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: Grimme C, Bossek J. Einführung in Die Optimierung - Konzepte, Methoden Und
Anwendungen. Springer Vieweg; 2018. doi:10.1007/978-3-658-21151-6
apa: Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen. Springer Vieweg. https://doi.org/10.1007/978-3-658-21151-6
bibtex: '@book{Grimme_Bossek_2018, title={Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen}, DOI={10.1007/978-3-658-21151-6},
publisher={Springer Vieweg}, author={Grimme, Christian and Bossek, Jakob}, year={2018}
}'
chicago: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung -
Konzepte, Methoden Und Anwendungen. Springer Vieweg, 2018. https://doi.org/10.1007/978-3-658-21151-6.
ieee: C. Grimme and J. Bossek, Einführung in die Optimierung - Konzepte, Methoden
und Anwendungen. Springer Vieweg, 2018.
mla: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung - Konzepte,
Methoden Und Anwendungen. Springer Vieweg, 2018, doi:10.1007/978-3-658-21151-6.
short: C. Grimme, J. Bossek, Einführung in Die Optimierung - Konzepte, Methoden
Und Anwendungen, Springer Vieweg, 2018.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:57Z
department:
- _id: '819'
doi: 10.1007/978-3-658-21151-6
extern: '1'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-658-21150-9
publisher: Springer Vieweg
status: public
title: Einführung in die Optimierung - Konzepte, Methoden und Anwendungen
type: book
user_id: '102979'
year: '2018'
...
---
_id: '48884'
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\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
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
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 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, 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}, 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 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,
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, 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 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...
---
_id: '48866'
abstract:
- lang: eng
text: 'Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Grapherator: A Modular Multi-Step Graph Generator. Journal of
Open Source Software. 2018;3(22):528. doi:10.21105/joss.00528'
apa: 'Bossek, J. (2018). Grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528. https://doi.org/10.21105/joss.00528'
bibtex: '@article{Bossek_2018, title={Grapherator: A Modular Multi-Step Graph Generator},
volume={3}, DOI={10.21105/joss.00528},
number={22}, journal={Journal of Open Source Software}, author={Bossek, Jakob},
year={2018}, pages={528} }'
chicago: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software 3, no. 22 (2018): 528. https://doi.org/10.21105/joss.00528.'
ieee: 'J. Bossek, “Grapherator: A Modular Multi-Step Graph Generator,” Journal
of Open Source Software, vol. 3, no. 22, p. 528, 2018, doi: 10.21105/joss.00528.'
mla: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software, vol. 3, no. 22, 2018, p. 528, doi:10.21105/joss.00528.'
short: J. Bossek, Journal of Open Source Software 3 (2018) 528.
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:51:50Z
department:
- _id: '819'
doi: 10.21105/joss.00528
intvolume: ' 3'
issue: '22'
language:
- iso: eng
page: '528'
publication: Journal of Open Source Software
publication_identifier:
issn:
- 2475-9066
status: public
title: 'Grapherator: A Modular Multi-Step Graph Generator'
type: journal_article
user_id: '102979'
volume: 3
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'
...