---
_id: '48873'
abstract:
- lang: eng
text: Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP)
heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful
in generating satisfactory or even optimal solutions. However, the reasons for
their success are not yet fully understood. Recent approaches take an analytical
viewpoint and try to identify instance features, which make an instance hard or
easy to solve. We contribute to this area by generating instance sets for couples
of TSP algorithms A and B by maximizing/minimizing their performance difference
in order to generate instances which are easier to solve for one solver and much
harder to solve for the other. This instance set offers the potential to identify
key features which allow to distinguish between the problem hardness classes of
both algorithms.
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
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J,
eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science.
Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4'
apa: Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann,
& J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp.
48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes
in Computer Science}, title={Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers}, DOI={10.1007/978-3-319-50349-3_4},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, Paola
and Sellmann, Meinolf and Vanschoren, Joaquin}, year={2016}, pages={48–59}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing
Performance Differences of State-of-the-Art Inexact TSP Solvers.” In Learning
and Intelligent Optimization, edited by Paola Festa, Meinolf Sellmann, and
Joaquin Vanschoren, 48–59. Lecture Notes in Computer Science. Cham: Springer International
Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_4.'
ieee: 'J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent
Optimization, 2016, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.'
mla: Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers.” Learning and Intelligent
Optimization, edited by Paola Festa et al., Springer International Publishing,
2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
short: 'J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.),
Learning and Intelligent Optimization, Springer International Publishing, Cham,
2016, pp. 48–59.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:05Z
department:
- _id: '819'
doi: 10.1007/978-3-319-50349-3_4
editor:
- first_name: Paola
full_name: Festa, Paola
last_name: Festa
- first_name: Meinolf
full_name: Sellmann, Meinolf
last_name: Sellmann
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
extern: '1'
keyword:
- Algorithm selection
- Feature selection
- Instance hardness
- TSP
language:
- iso: eng
page: 48–59
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-319-50349-3
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Evolving Instances for Maximizing Performance Differences of State-of-the-Art
Inexact TSP Solvers
type: conference
user_id: '102979'
year: '2016'
...
---
_id: '48874'
abstract:
- lang: eng
text: State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem
TSP are known to mostly yield high-quality solutions in reasonable computation
times. With the purpose of understanding different levels of instance difficulties,
instances for the current State of the Art heuristic TSP solvers LKH+restart and
EAX+restart are presented which are evolved using a sophisticated evolutionary
algorithm. More specifically, the performance differences of the respective solvers
are maximized resulting in instances which are easier to solve for one solver
and much more difficult for the other. Focusing on both optimization directions,
instance features are identified which characterize both types of instances and
increase the understanding of solver performance differences.
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
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
In: Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037.
AI*IA 2016. Springer-Verlag; 2016:3–12. doi:10.1007/978-3-319-49130-1_1'
apa: Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
Proceedings of the XV International Conference of the Italian Association for
Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
3–12. https://doi.org/10.1007/978-3-319-49130-1_1
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Berlin, Heidelberg}, series={AI*IA
2016}, title={Understanding Characteristics of Evolved Instances for State-of-the-Art
Inexact TSP Solvers with Maximum Performance Difference}, DOI={10.1007/978-3-319-49130-1_1},
booktitle={Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037},
publisher={Springer-Verlag}, author={Bossek, Jakob and Trautmann, Heike}, year={2016},
pages={3–12}, collection={AI*IA 2016} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of
Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance
Difference.” In Proceedings of the XV International Conference of the Italian
Association for Artificial Intelligence on Advances in Artificial Intelligence
- Volume 10037, 3–12. AI*IA 2016. Berlin, Heidelberg: Springer-Verlag, 2016.
https://doi.org/10.1007/978-3-319-49130-1_1.'
ieee: 'J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,”
in Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
2016, pp. 3–12, doi: 10.1007/978-3-319-49130-1_1.'
mla: Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.”
Proceedings of the XV International Conference of the Italian Association for
Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
Springer-Verlag, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1.
short: 'J. Bossek, H. Trautmann, in: Proceedings of the XV International Conference
of the Italian Association for Artificial Intelligence on Advances in Artificial
Intelligence - Volume 10037, Springer-Verlag, Berlin, Heidelberg, 2016, pp. 3–12.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:11Z
doi: 10.1007/978-3-319-49130-1_1
extern: '1'
keyword:
- Combinatorial optimization
- Instance hardness
- Metaheuristics
- Transportation
- TSP
language:
- iso: eng
page: 3–12
place: Berlin, Heidelberg
publication: Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037
publication_identifier:
isbn:
- 978-3-319-49129-5
publication_status: published
publisher: Springer-Verlag
series_title: AI*IA 2016
status: public
title: Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact
TSP Solvers with Maximum Performance Difference
type: conference
user_id: '102979'
year: '2016'
...
---
_id: '46365'
abstract:
- lang: eng
text: Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP)
heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful
in generating satisfactory or even optimal solutions. However, the reasons for
their success are not yet fully understood. Recent approaches take an analytical
viewpoint and try to identify instance features, which make an instance hard or
easy to solve. We contribute to this area by generating instance sets for couples
of TSP algorithms A and B by maximizing/minimizing their performance difference
in order to generate instances which are easier to solve for one solver and much
harder to solve for the other. This instance set offers the potential to identify
key features which allow to distinguish between the problem hardness classes of
both algorithms.
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. Evolving Instances for Maximizing Performance Differences
of State-of-The-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J,
eds. Learning and Intelligent Optimization. Vol 10079. Lecture Notes in
Computer Science. Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4'
apa: Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance
Differences of State-of-The-Art Inexact TSP Solvers. In P. Festa, M. Sellmann,
& J. Vanschoren (Eds.), Learning and Intelligent Optimization (Vol.
10079, pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Ischia, Italy}, series={Lecture
Notes in Computer Science}, title={Evolving Instances for Maximizing Performance
Differences of State-of-The-Art Inexact TSP Solvers}, volume={10079}, DOI={10.1007/978-3-319-50349-3_4},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, P and
Sellmann, M and Vanschoren, J}, year={2016}, pages={48–59}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing
Performance Differences of State-of-The-Art Inexact TSP Solvers.” In Learning
and Intelligent Optimization, edited by P Festa, M Sellmann, and J Vanschoren,
10079:48–59. Lecture Notes in Computer Science. Ischia, Italy: Springer International
Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_4.'
ieee: 'J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance
Differences of State-of-The-Art Inexact TSP Solvers,” in Learning and Intelligent
Optimization, 2016, vol. 10079, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.'
mla: Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance
Differences of State-of-The-Art Inexact TSP Solvers.” Learning and Intelligent
Optimization, edited by P Festa et al., vol. 10079, Springer International
Publishing, 2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
short: 'J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.),
Learning and Intelligent Optimization, Springer International Publishing, Ischia,
Italy, 2016, pp. 48–59.'
date_created: 2023-08-04T15:10:58Z
date_updated: 2024-06-10T11:58:25Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-50349-3_4
editor:
- first_name: P
full_name: Festa, P
last_name: Festa
- first_name: M
full_name: Sellmann, M
last_name: Sellmann
- first_name: J
full_name: Vanschoren, J
last_name: Vanschoren
intvolume: ' 10079'
language:
- iso: eng
page: 48–59
place: Ischia, Italy
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-319-50348-6
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Evolving Instances for Maximizing Performance Differences of State-of-The-Art
Inexact TSP Solvers
type: conference
user_id: '15504'
volume: 10079
year: '2016'
...
---
_id: '46366'
abstract:
- lang: eng
text: State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem
(TSP) are known to mostly yield high-quality solutions in reasonable computation
times. With the purpose of understanding different levels of instance difficulties,
instances for the current State of the Art heuristic TSP solvers LKH+restart and
EAX+restart are presented which are evolved using a sophisticated evolutionary
algorithm. More specifically, the performance differences of the respective solvers
are maximized resulting in instances which are easier to solve for one solver
and much more difficult for the other. Focusing on both optimization directions,
instance features are identified which characterize both types of instances and
increase the understanding of solver performance differences.
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. Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
In: Adorni G, Cagnoni S, Gori M, Maratea M, eds. AI*IA 2016 Advances in Artificial
Intelligence. Vol 10037. Lecture Notes in Computer Science. Springer; 2016:3–12.
doi:10.1007/978-3-319-49130-1_1'
apa: Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances
in Artificial Intelligence (Vol. 10037, pp. 3–12). Springer. https://doi.org/10.1007/978-3-319-49130-1_1
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes
in Computer Science}, title={Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference},
volume={10037}, DOI={10.1007/978-3-319-49130-1_1},
booktitle={AI*IA 2016 Advances in Artificial Intelligence}, publisher={Springer},
author={Bossek, Jakob and Trautmann, Heike}, editor={Adorni, G and Cagnoni, S
and Gori, M and Maratea, M}, year={2016}, pages={3–12}, collection={Lecture Notes
in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of
Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance
Difference.” In AI*IA 2016 Advances in Artificial Intelligence, edited
by G Adorni, S Cagnoni, M Gori, and M Maratea, 10037:3–12. Lecture Notes in Computer
Science. Cham: Springer, 2016. https://doi.org/10.1007/978-3-319-49130-1_1.'
ieee: 'J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,”
in AI*IA 2016 Advances in Artificial Intelligence, 2016, vol. 10037, pp.
3–12, doi: 10.1007/978-3-319-49130-1_1.'
mla: Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.”
AI*IA 2016 Advances in Artificial Intelligence, edited by G Adorni et al.,
vol. 10037, Springer, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1.
short: 'J. Bossek, H. Trautmann, in: G. Adorni, S. Cagnoni, M. Gori, M. Maratea
(Eds.), AI*IA 2016 Advances in Artificial Intelligence, Springer, Cham, 2016,
pp. 3–12.'
date_created: 2023-08-04T15:11:47Z
date_updated: 2024-06-10T11:58:12Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-49130-1_1
editor:
- first_name: G
full_name: Adorni, G
last_name: Adorni
- first_name: S
full_name: Cagnoni, S
last_name: Cagnoni
- first_name: M
full_name: Gori, M
last_name: Gori
- first_name: M
full_name: Maratea, M
last_name: Maratea
intvolume: ' 10037'
language:
- iso: eng
page: 3–12
place: Cham
publication: AI*IA 2016 Advances in Artificial Intelligence
publication_identifier:
isbn:
- 978-3-319-49129-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact
TSP Solvers with Maximum Performance Difference
type: conference
user_id: '15504'
volume: 10037
year: '2016'
...
---
_id: '46373'
abstract:
- lang: eng
text: The need for automatic methods of topic discovery in the Internet grows exponentially
with the amount of available textual information. Nowadays it becomes impossible
to manually read even a small part of the information in order to reveal the underlying
topics. Social media provide us with a great pool of user generated content, where
topic discovery may be extremely useful for businesses, politicians, researchers,
and other stakeholders. However, conventional topic discovery methods, which are
widely used in large text corpora, face several challenges when they are applied
in social media and particularly in Twitter – the most popular microblogging platform.
To the best of our knowledge no comprehensive overview of these challenges and
of the methods dedicated to address these challenges does exist in IS literature
until now. Therefore, this paper provides an overview of these challenges, matching
methods and their expected usefulness for social media analytics.
author:
- first_name: Andrey
full_name: Chinnov, Andrey
last_name: Chinnov
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Christian
full_name: Meske, Christian
last_name: Meske
- first_name: Stefan
full_name: Stieglitz, Stefan
last_name: Stieglitz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Chinnov A, Kerschke P, Meske C, Stieglitz S, Trautmann H. An Overview of Topic
Discovery in Twitter Communication through Social Media Analytics. In: Proceedings
of the 20$^th$ Americas Conference on Information Systems (AMCIS ’15). ; 2015:1–10.'
apa: Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., & Trautmann, H. (2015).
An Overview of Topic Discovery in Twitter Communication through Social Media Analytics.
Proceedings of the 20$^th$ Americas Conference on Information Systems (AMCIS
’15), 1–10.
bibtex: '@inproceedings{Chinnov_Kerschke_Meske_Stieglitz_Trautmann_2015, place={Puerto
Rico}, title={An Overview of Topic Discovery in Twitter Communication through
Social Media Analytics}, booktitle={Proceedings of the 20$^th$ Americas Conference
on Information Systems (AMCIS ’15)}, author={Chinnov, Andrey and Kerschke, Pascal
and Meske, Christian and Stieglitz, Stefan and Trautmann, Heike}, year={2015},
pages={1–10} }'
chicago: Chinnov, Andrey, Pascal Kerschke, Christian Meske, Stefan Stieglitz, and
Heike Trautmann. “An Overview of Topic Discovery in Twitter Communication through
Social Media Analytics.” In Proceedings of the 20$^th$ Americas Conference
on Information Systems (AMCIS ’15), 1–10. Puerto Rico, 2015.
ieee: A. Chinnov, P. Kerschke, C. Meske, S. Stieglitz, and H. Trautmann, “An Overview
of Topic Discovery in Twitter Communication through Social Media Analytics,” in
Proceedings of the 20$^th$ Americas Conference on Information Systems (AMCIS
’15), 2015, pp. 1–10.
mla: Chinnov, Andrey, et al. “An Overview of Topic Discovery in Twitter Communication
through Social Media Analytics.” Proceedings of the 20$^th$ Americas Conference
on Information Systems (AMCIS ’15), 2015, pp. 1–10.
short: 'A. Chinnov, P. Kerschke, C. Meske, S. Stieglitz, H. Trautmann, in: Proceedings
of the 20$^th$ Americas Conference on Information Systems (AMCIS ’15), Puerto
Rico, 2015, pp. 1–10.'
date_created: 2023-08-04T15:20:52Z
date_updated: 2023-10-16T13:41:00Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 1–10
place: Puerto Rico
publication: Proceedings of the 20$^th$ Americas Conference on Information Systems
(AMCIS ’15)
publication_identifier:
isbn:
- 978-0-9966831-0-4
status: public
title: An Overview of Topic Discovery in Twitter Communication through Social Media
Analytics
type: conference
user_id: '15504'
year: '2015'
...
---
_id: '46375'
abstract:
- lang: eng
text: In single-objective optimization different optimization strategies exist depending
on the structure and characteristics of the underlying problem. In particular,
the presence of so-called funnels in multimodal problems offers the possibility
of applying techniques exploiting the global structure of the function. The recently
proposed Exploratory Landscape Analysis approach automatically identifies problem
characteristics based on a moderately small initial sample of the objective function
and proved to be effective for algorithm selection problems in continuous black-box
optimization. In this paper, specific features for detecting funnel structures
are introduced and combined with the existing ones in order to classify optimization
problems regarding the funnel property. The effectiveness of the approach is shown
by experiments on specifically generated test instances and validation experiments
on standard benchmark 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. Detecting Funnel Structures
by Means of Exploratory Landscape Analysis. In: Silva S, ed. Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO ’15). ACM; 2015:265–272.
doi:10.1145/2739480.2754642'
apa: Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015). Detecting
Funnel Structures by Means of Exploratory Landscape Analysis. In S. Silva (Ed.),
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)
(pp. 265–272). ACM. https://doi.org/10.1145/2739480.2754642
bibtex: '@inproceedings{Kerschke_Preuss_Wessing_Trautmann_2015, place={New York,
NY, USA}, title={Detecting Funnel Structures by Means of Exploratory Landscape
Analysis}, DOI={10.1145/2739480.2754642},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’15)}, publisher={ACM}, author={Kerschke, Pascal and Preuss, Mike and Wessing,
Simon and Trautmann, Heike}, editor={Silva, Sara}, year={2015}, pages={265–272}
}'
chicago: 'Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. “Detecting
Funnel Structures by Means of Exploratory Landscape Analysis.” In Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’15), edited
by Sara Silva, 265–272. New York, NY, USA: ACM, 2015. https://doi.org/10.1145/2739480.2754642.'
ieee: 'P. Kerschke, M. Preuss, S. Wessing, and H. Trautmann, “Detecting Funnel Structures
by Means of Exploratory Landscape Analysis,” in Proceedings of the Genetic
and Evolutionary Computation Conference (GECCO ’15), 2015, pp. 265–272, doi:
10.1145/2739480.2754642.'
mla: Kerschke, Pascal, et al. “Detecting Funnel Structures by Means of Exploratory
Landscape Analysis.” Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO ’15), edited by Sara Silva, ACM, 2015, pp. 265–272, doi:10.1145/2739480.2754642.
short: 'P. Kerschke, M. Preuss, S. Wessing, H. Trautmann, in: S. Silva (Ed.), Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’15), ACM, New York,
NY, USA, 2015, pp. 265–272.'
date_created: 2023-08-04T15:22:39Z
date_updated: 2023-10-16T13:41:38Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2739480.2754642
editor:
- first_name: Sara
full_name: Silva, Sara
last_name: Silva
language:
- iso: eng
page: 265–272
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’15)
publication_identifier:
isbn:
- 978-1-4503-3472-3
publisher: ACM
status: public
title: Detecting Funnel Structures by Means of Exploratory Landscape Analysis
type: conference
user_id: '15504'
year: '2015'
...
---
_id: '46376'
abstract:
- lang: eng
text: We investigate per-instance algorithm selection techniques for solving the
Travelling Salesman Problem (TSP), based on the two state-of-the-art inexact TSP
solvers, LKH and EAX. Our comprehensive experiments demonstrate that the solvers
exhibit complementary performance across a diverse set of instances, and the potential
for improving the state of the art by selecting between them is significant. Using
TSP features from the literature as well as a set of novel features, we show that
we can capitalise on this potential by building an efficient selector that achieves
significant performance improvements in practice. Our selectors represent a significant
improvement in the state-of-the-art in inexact TSP solving, and hence in the ability
to find optimal solutions (without proof of optimality) for challenging TSP instances
in practice.
author:
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Holger
full_name: Hoos, Holger
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kotthoff L, Kerschke P, Hoos H, Trautmann H. Improving the State of the Art
in Inexact TSP Solving Using Per-Instance Algorithm Selection. In: Dhaenens C,
Jourdan L, Marmion M-E, eds. Learning and Intelligent Optimization. Springer
International Publishing; 2015:202–217.'
apa: Kotthoff, L., Kerschke, P., Hoos, H., & Trautmann, H. (2015). Improving
the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection.
In C. Dhaenens, L. Jourdan, & M.-E. Marmion (Eds.), Learning and Intelligent
Optimization (pp. 202–217). Springer International Publishing.
bibtex: '@inproceedings{Kotthoff_Kerschke_Hoos_Trautmann_2015, place={Cham}, title={Improving
the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Kotthoff, Lars and Kerschke, Pascal and Hoos, Holger and
Trautmann, Heike}, editor={Dhaenens, Clarisse and Jourdan, Laetitia and Marmion,
Marie-Eléonore}, year={2015}, pages={202–217} }'
chicago: 'Kotthoff, Lars, Pascal Kerschke, Holger Hoos, and Heike Trautmann. “Improving
the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection.”
In Learning and Intelligent Optimization, edited by Clarisse Dhaenens,
Laetitia Jourdan, and Marie-Eléonore Marmion, 202–217. Cham: Springer International
Publishing, 2015.'
ieee: L. Kotthoff, P. Kerschke, H. Hoos, and H. Trautmann, “Improving the State
of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection,” in
Learning and Intelligent Optimization, 2015, pp. 202–217.
mla: Kotthoff, Lars, et al. “Improving the State of the Art in Inexact TSP Solving
Using Per-Instance Algorithm Selection.” Learning and Intelligent Optimization,
edited by Clarisse Dhaenens et al., Springer International Publishing, 2015, pp.
202–217.
short: 'L. Kotthoff, P. Kerschke, H. Hoos, H. Trautmann, in: C. Dhaenens, L. Jourdan,
M.-E. Marmion (Eds.), Learning and Intelligent Optimization, Springer International
Publishing, Cham, 2015, pp. 202–217.'
date_created: 2023-08-04T15:24:20Z
date_updated: 2023-10-16T13:41:54Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Clarisse
full_name: Dhaenens, Clarisse
last_name: Dhaenens
- first_name: Laetitia
full_name: Jourdan, Laetitia
last_name: Jourdan
- first_name: Marie-Eléonore
full_name: Marmion, Marie-Eléonore
last_name: Marmion
language:
- iso: eng
page: 202–217
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-319-19084-6
publisher: Springer International Publishing
status: public
title: Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm
Selection
type: conference
user_id: '15504'
year: '2015'
...
---
_id: '46379'
abstract:
- lang: eng
text: In multiobjective optimization, set-based performance indicators are commonly
used to assess the quality of a Pareto front approximation. Based on the scalarization
obtained by these indicators, a performance comparison of multiobjective optimization
algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent
two recommended approaches which have shown a correlated behavior in recent empirical
studies. Whereas the HV indicator has been comprehensively analyzed in the last
years, almost no studies on the R2 indicator exist. In this extended version of
our previous conference paper, we thus perform a comprehensive investigation of
the properties of the R2 indicator in a theoretical and empirical way. The influence
of the number and distribution of the weight vectors on the optimal distribution
of µ solutions is analyzed. Based on a comparative analysis, specific characteristics
and differences of the R2 and HV indicator are presented. Furthermore, the R2
indicator is integrated into an indicator-based steady-state evolutionary multiobjective
optimization algorithm (EMOA). It is shown that the so-called R2-EMOA can accurately
approximate the optimal distribution of µ solutions regarding R2.
author:
- first_name: D
full_name: Brockhoff, D
last_name: Brockhoff
- first_name: T
full_name: Wagner, T
last_name: Wagner
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Brockhoff D, Wagner T, Trautmann H. R2 Indicator Based Multiobjective Search.
Evolutionary Computation Journal. 2015;23(3):369–395. doi:10.1162/EVCO_a_00135
apa: Brockhoff, D., Wagner, T., & Trautmann, H. (2015). R2 Indicator Based Multiobjective
Search. Evolutionary Computation Journal, 23(3), 369–395. https://doi.org/10.1162/EVCO_a_00135
bibtex: '@article{Brockhoff_Wagner_Trautmann_2015, title={R2 Indicator Based Multiobjective
Search}, volume={23}, DOI={10.1162/EVCO_a_00135},
number={3}, journal={Evolutionary Computation Journal}, author={Brockhoff, D and
Wagner, T and Trautmann, Heike}, year={2015}, pages={369–395} }'
chicago: 'Brockhoff, D, T Wagner, and Heike Trautmann. “R2 Indicator Based Multiobjective
Search.” Evolutionary Computation Journal 23, no. 3 (2015): 369–395. https://doi.org/10.1162/EVCO_a_00135.'
ieee: 'D. Brockhoff, T. Wagner, and H. Trautmann, “R2 Indicator Based Multiobjective
Search,” Evolutionary Computation Journal, vol. 23, no. 3, pp. 369–395,
2015, doi: 10.1162/EVCO_a_00135.'
mla: Brockhoff, D., et al. “R2 Indicator Based Multiobjective Search.” Evolutionary
Computation Journal, vol. 23, no. 3, 2015, pp. 369–395, doi:10.1162/EVCO_a_00135.
short: D. Brockhoff, T. Wagner, H. Trautmann, Evolutionary Computation Journal 23
(2015) 369–395.
date_created: 2023-08-04T15:28:25Z
date_updated: 2023-10-16T13:42:47Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/EVCO_a_00135
intvolume: ' 23'
issue: '3'
language:
- iso: eng
page: 369–395
publication: Evolutionary Computation Journal
status: public
title: R2 Indicator Based Multiobjective Search
type: journal_article
user_id: '15504'
volume: 23
year: '2015'
...
---
_id: '46374'
abstract:
- lang: eng
text: "We consider a routing problem for a single vehicle serving customer Locations
in the course of time. A subset of these customers must necessarily be served,
while the complement of this subset contains dynamic customers which request for
service over time, and which do not necessarily need to be served. The decision
maker’s conflicting goals are serving as many customers as possible as well as
minimizing total travel distance. We solve this bi-objective Problem with an evolutionary
multi-objective algorithm in order to provide an a-posteriori evaluation tool
for enabling decision makers to assess the single objective solution strategies
that they actually use in real-time. We present the modifications to be applied
to the evolutionary multi-objective algorithm NSGA2 in order to solve the routing
problem, we describe a number of real-time single-objective solution strategies,
and we finally use the gained efficient trade-off solutions of NSGA2 to exemplarily
evaluate the real-time strategies. Our results show that the evolutionary multi-objective
approach is well-suited to generate benchmarks for assessing dynamic heuristic
strategies. Our findings point into future directions for designing dynamic multi-objective
approaches for the vehicle routing problem with time windows.\r\n"
author:
- first_name: C
full_name: Grimme, C
last_name: Grimme
- first_name: S
full_name: Meisel, S
last_name: Meisel
- 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: M
full_name: Wölck, M
last_name: Wölck
citation:
ama: 'Grimme C, Meisel S, Trautmann H, Rudolph G, Wölck M. Multi-Objective Analysis
of Approaches to Dynamic Routing Of a Vehicle. In: Proceedings of the European
Conference On Information Systems. ; 2015.'
apa: Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., & Wölck, M. (2015).
Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle. Proceedings
of the European Conference On Information Systems.
bibtex: '@inproceedings{Grimme_Meisel_Trautmann_Rudolph_Wölck_2015, place={Münster,
Germany}, title={Multi-Objective Analysis of Approaches to Dynamic Routing Of
a Vehicle}, booktitle={Proceedings of the European Conference On Information Systems},
author={Grimme, C and Meisel, S and Trautmann, Heike and Rudolph, G and Wölck,
M}, year={2015} }'
chicago: Grimme, C, S Meisel, Heike Trautmann, G Rudolph, and M Wölck. “Multi-Objective
Analysis of Approaches to Dynamic Routing Of a Vehicle.” In Proceedings of
the European Conference On Information Systems. Münster, Germany, 2015.
ieee: C. Grimme, S. Meisel, H. Trautmann, G. Rudolph, and M. Wölck, “Multi-Objective
Analysis of Approaches to Dynamic Routing Of a Vehicle,” 2015.
mla: Grimme, C., et al. “Multi-Objective Analysis of Approaches to Dynamic Routing
Of a Vehicle.” Proceedings of the European Conference On Information Systems,
2015.
short: 'C. Grimme, S. Meisel, H. Trautmann, G. Rudolph, M. Wölck, in: Proceedings
of the European Conference On Information Systems, Münster, Germany, 2015.'
date_created: 2023-08-04T15:21:44Z
date_updated: 2023-10-16T13:41:16Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
place: Münster, Germany
publication: Proceedings of the European Conference On Information Systems
status: public
title: Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle
type: conference
user_id: '15504'
year: '2015'
...
---
_id: '46380'
abstract:
- lang: eng
text: 'We present methods to answer two basic questions that arise when benchmarking
optimization algorithms. The first one is: which algorithm is the "best" one?
and the second one is: which algorithm should I use for my real-world problem?
Both are connected and neither is easy to answer. We present a theoretical framework
for designing and analyzing the raw data of such benchmark experiments. This represents
a first step in answering the aforementioned questions. The 2009 and 2010 BBOB
benchmark results are analyzed by means of this framework and we derive insight
regarding the answers to the two questions. Furthermore, we discuss how to properly
aggregate rankings from algorithm evaluations on individual problems into a consensus,
its theoretical background and which common pitfalls should be avoided. Finally,
we address the grouping of test problems into sets with similar optimizer rankings
and investigate whether these are reflected by already proposed test problem characteristics,
finding that this is not always the case.'
author:
- first_name: O
full_name: Mersmann, O
last_name: Mersmann
- first_name: M
full_name: Preuss, M
last_name: Preuss
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: B
full_name: Bischl, B
last_name: Bischl
- first_name: C
full_name: Weihs, C
last_name: Weihs
citation:
ama: Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C. Analyzing the BBOB Results
by Means of Benchmarking Concepts. Evolutionary Computation Journal. 2015;23(1):161–185.
apa: Mersmann, O., Preuss, M., Trautmann, H., Bischl, B., & Weihs, C. (2015).
Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary
Computation Journal, 23(1), 161–185.
bibtex: '@article{Mersmann_Preuss_Trautmann_Bischl_Weihs_2015, title={Analyzing
the BBOB Results by Means of Benchmarking Concepts}, volume={23}, number={1},
journal={Evolutionary Computation Journal}, author={Mersmann, O and Preuss, M
and Trautmann, Heike and Bischl, B and Weihs, C}, year={2015}, pages={161–185}
}'
chicago: 'Mersmann, O, M Preuss, Heike Trautmann, B Bischl, and C Weihs. “Analyzing
the BBOB Results by Means of Benchmarking Concepts.” Evolutionary Computation
Journal 23, no. 1 (2015): 161–185.'
ieee: O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, and C. Weihs, “Analyzing
the BBOB Results by Means of Benchmarking Concepts,” Evolutionary Computation
Journal, vol. 23, no. 1, pp. 161–185, 2015.
mla: Mersmann, O., et al. “Analyzing the BBOB Results by Means of Benchmarking Concepts.”
Evolutionary Computation Journal, vol. 23, no. 1, 2015, pp. 161–185.
short: O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, C. Weihs, Evolutionary Computation
Journal 23 (2015) 161–185.
date_created: 2023-08-04T15:30:11Z
date_updated: 2023-10-16T13:43:06Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 23'
issue: '1'
language:
- iso: eng
page: 161–185
publication: Evolutionary Computation Journal
status: public
title: Analyzing the BBOB Results by Means of Benchmarking Concepts
type: journal_article
user_id: '15504'
volume: 23
year: '2015'
...
---
_id: '48838'
abstract:
- lang: eng
text: 'The majority of algorithms can be controlled or adjusted by parameters. Their
values can substantially affect the algorithms’ performance. Since the manual
exploration of the parameter space is tedious – even for few parameters – several
automatic procedures for parameter tuning have been proposed. Recent approaches
also take into account some characteristic properties of the problem instances,
frequently termed instance features. Our contribution is the proposal of a novel
concept for feature-based algorithm parameter tuning, which applies an approximating
surrogate model for learning the continuous feature-parameter mapping. To accomplish
this, we learn a joint model of the algorithm performance based on both the algorithm
parameters and the instance features. The required data is gathered using a recently
proposed acquisition function for model refinement in surrogate-based optimization:
the profile expected improvement. This function provides an avenue for maximizing
the information required for the feature-parameter mapping, i.e., the mapping
from instance features to the corresponding optimal algorithm parameters. The
approach is validated by applying the tuner to exemplary evolutionary algorithms
and problems, for which theoretically grounded or heuristically determined feature-parameter
mappings are available.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
citation:
ama: 'Bossek J, Bischl B, Wagner T, Rudolph G. Learning Feature-Parameter Mappings
for Parameter Tuning via the Profile Expected Improvement. In: Proceedings
of the Genetic and Evolutionary Computation Conference. GECCO ’15. Association
for Computing Machinery; 2015:1319–1326. doi:10.1145/2739480.2754673'
apa: Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter
Mappings for Parameter Tuning via the Profile Expected Improvement. Proceedings
of the Genetic and Evolutionary Computation Conference, 1319–1326. https://doi.org/10.1145/2739480.2754673
bibtex: '@inproceedings{Bossek_Bischl_Wagner_Rudolph_2015, place={New York, NY,
USA}, series={GECCO ’15}, title={Learning Feature-Parameter Mappings for Parameter
Tuning via the Profile Expected Improvement}, DOI={10.1145/2739480.2754673},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Bischl,
Bernd and Wagner, Tobias and Rudolph, Günter}, year={2015}, pages={1319–1326},
collection={GECCO ’15} }'
chicago: 'Bossek, Jakob, Bernd Bischl, Tobias Wagner, and Günter Rudolph. “Learning
Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1319–1326. GECCO ’15. New York, NY, USA: Association for Computing Machinery,
2015. https://doi.org/10.1145/2739480.2754673.'
ieee: 'J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter
Mappings for Parameter Tuning via the Profile Expected Improvement,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2015, pp. 1319–1326,
doi: 10.1145/2739480.2754673.'
mla: Bossek, Jakob, et al. “Learning Feature-Parameter Mappings for Parameter Tuning
via the Profile Expected Improvement.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2015, pp. 1319–1326,
doi:10.1145/2739480.2754673.
short: 'J. Bossek, B. Bischl, T. Wagner, G. Rudolph, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2015, pp. 1319–1326.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:40:30Z
department:
- _id: '819'
doi: 10.1145/2739480.2754673
extern: '1'
keyword:
- evolutionary algorithms
- model-based optimization
- parameter tuning
language:
- iso: eng
page: 1319–1326
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-3472-3
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’15
status: public
title: Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected
Improvement
type: conference
user_id: '102979'
year: '2015'
...
---
_id: '48887'
abstract:
- lang: eng
text: 'We evaluate the performance of a multi-objective evolutionary algorithm on
a class of dynamic routing problems with a single vehicle. In particular we focus
on relating algorithmic performance to the most prominent characteristics of problem
instances. The routing problem considers two types of customers: mandatory customers
must be visited whereas optional customers do not necessarily have to be visited.
Moreover, mandatory customers are known prior to the start of the tour whereas
optional customers request for service at later points in time with the vehicle
already being on its way. The multi-objective optimization problem then results
as maximizing the number of visited customers while simultaneously minimizing
total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm
aims at approximating the related Pareto set for specifically designed benchmarking
instances differing in terms of number of customers, geographical layout, fraction
of mandatory customers, and request times of optional customers. Conceptional
and experimental comparisons to online heuristic procedures are provided.'
author:
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- 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
- first_name: Martin
full_name: Wölck, Martin
last_name: Wölck
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation
of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.
In: Proceedings of the Genetic and Evolutionary Computation Conference .
GECCO’15. Association for Computing Machinery; 2015:425–432. doi:10.1145/2739480.2754705'
apa: Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann,
H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic
Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation
Conference , 425–432. https://doi.org/10.1145/2739480.2754705
bibtex: '@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={New
York, NY, USA}, series={GECCO’15}, title={Evaluation of a Multi-Objective EA on
Benchmark Instances for Dynamic Routing of a Vehicle}, DOI={10.1145/2739480.2754705},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
}, publisher={Association for Computing Machinery}, author={Meisel, Stephan and
Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and
Trautmann, Heike}, year={2015}, pages={425–432}, collection={GECCO’15} }'
chicago: 'Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Günter
Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark
Instances for Dynamic Routing of a Vehicle.” In Proceedings of the Genetic
and Evolutionary Computation Conference , 425–432. GECCO’15. New York, NY,
USA: Association for Computing Machinery, 2015. https://doi.org/10.1145/2739480.2754705.'
ieee: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann,
“Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
of a Vehicle,” in Proceedings of the Genetic and Evolutionary Computation Conference
, 2015, pp. 425–432, doi: 10.1145/2739480.2754705.'
mla: Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances
for Dynamic Routing of a Vehicle.” Proceedings of the Genetic and Evolutionary
Computation Conference , Association for Computing Machinery, 2015, pp. 425–432,
doi:10.1145/2739480.2754705.
short: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in:
Proceedings of the Genetic and Evolutionary Computation Conference , Association
for Computing Machinery, New York, NY, USA, 2015, pp. 425–432.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:06Z
department:
- _id: '819'
doi: 10.1145/2739480.2754705
extern: '1'
keyword:
- combinatorial optimization
- metaheuristics
- multi-objective optimization
- online algorithms
- transportation
language:
- iso: eng
page: 425–432
place: New York, NY, USA
publication: 'Proceedings of the Genetic and Evolutionary Computation Conference '
publication_identifier:
isbn:
- 978-1-4503-3472-3
publisher: Association for Computing Machinery
series_title: GECCO’15
status: public
title: Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
of a Vehicle
type: conference
user_id: '102979'
year: '2015'
...
---
_id: '46377'
abstract:
- lang: eng
text: 'We evaluate the performance of a multi-objective evolutionary algorithm on
a class of dynamic routing problems with a single vehicle. In particular we focus
on relating algorithmic performance to the most prominent characteristics of problem
instances. The routing problem considers two types of customers: mandatory customers
must be visited whereas optional customers do not necessarily have to be visited.
Moreover, mandatory customers are known prior to the start of the tour whereas
optional customers request for service at later points in time with the vehicle
already being on its way. The multi-objective optimization problem then results
as maximizing the number of visited customers while simultaneously minimizing
total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm
aims at approximating the related Pareto set for specifically designed benchmarking
instances differing in terms of number of customers, geographical layout, fraction
of mandatory customers, and request times of optional customers. Conceptional
and experimental comparisons to online heuristic procedures are provided.'
author:
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- 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
- first_name: Martin
full_name: Wölck, Martin
last_name: Wölck
- 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: 'Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation
of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’15). ; 2015:425–432. doi:10.1145/2739480.2754705'
apa: Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann,
H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic
Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO ’15), 425–432. https://doi.org/10.1145/2739480.2754705
bibtex: '@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={Madrid,
Spain}, title={Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic
Routing of a Vehicle}, DOI={10.1145/2739480.2754705},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’15)}, author={Meisel, Stephan and Grimme, Christian and Bossek, Jakob
and Wölck, Martin and Rudolph, Guenter and Trautmann, Heike}, year={2015}, pages={425–432}
}'
chicago: Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Guenter
Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark
Instances for Dynamic Routing of a Vehicle.” In Proceedings of the Genetic
and Evolutionary Computation Conference (GECCO ’15), 425–432. Madrid, Spain,
2015. https://doi.org/10.1145/2739480.2754705.
ieee: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann,
“Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
of a Vehicle,” in Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’15), 2015, pp. 425–432, doi: 10.1145/2739480.2754705.'
mla: Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances
for Dynamic Routing of a Vehicle.” Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO ’15), 2015, pp. 425–432, doi:10.1145/2739480.2754705.
short: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15),
Madrid, Spain, 2015, pp. 425–432.'
date_created: 2023-08-04T15:24:41Z
date_updated: 2024-06-10T11:57:57Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2739480.2754705
language:
- iso: eng
page: 425–432
place: Madrid, Spain
publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’15)
publication_identifier:
isbn:
- 978-1-4503-3472-3
status: public
title: Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
of a Vehicle
type: conference
user_id: '15504'
year: '2015'
...
---
_id: '46381'
abstract:
- lang: eng
text: Exploratory Landscape Analysis is an effective and sophisticated approach
to characterize the properties of continuous optimization problems. The overall
aim is to exploit this knowledge to give recommendations of the individually best
suited algorithm for unseen optimization problems. Recent research revealed a
high potential of this methodology in this respect based on a set of well-defined,
computable features which only requires a quite small sample of function evaluations.
In this paper, new features based on the cell mapping concept are introduced and
shown to improve the existing feature set in terms of predicting expert-designed
high-level properties, such as the degree of multimodality or the global structure,
for 2-dimensional single objective optimization problems.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Carlos
full_name: Hernández, Carlos
last_name: Hernández
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
- first_name: Jian-Qiao
full_name: Sun, Jian-Qiao
last_name: Sun
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Preuss M, Hernández C, et al. Cell Mapping Techniques for Exploratory
Landscape Analysis. In: Tantar A-A, Tantar E, Sun J-Q, et al., eds. EVOLVE
— A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation
V. Vol 288. Advances in Intelligent Systems and Computing. Springer International
Publishing; 2014:115–131. doi:10.1007/978-3-319-07494-8_9'
apa: Kerschke, P., Preuss, M., Hernández, C., Schütze, O., Sun, J.-Q., Grimme, C.,
Rudolph, G., Bischl, B., & Trautmann, H. (2014). Cell Mapping Techniques for
Exploratory Landscape Analysis. In A.-A. Tantar, E. Tantar, J.-Q. Sun, W. Zhang,
Q. Ding, O. Schütze, M. T. M. Emmerich, P. Legrand, M. P. Del, & C. C. A.
Coello (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics,
and Evolutionary Computation V (Vol. 288, pp. 115–131). Springer International
Publishing. https://doi.org/10.1007/978-3-319-07494-8_9
bibtex: '@inbook{Kerschke_Preuss_Hernández_Schütze_Sun_Grimme_Rudolph_Bischl_Trautmann_2014,
place={Cham}, series={Advances in Intelligent Systems and Computing}, title={Cell
Mapping Techniques for Exploratory Landscape Analysis}, volume={288}, DOI={10.1007/978-3-319-07494-8_9},
booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V}, publisher={Springer International Publishing}, author={Kerschke,
Pascal and Preuss, Mike and Hernández, Carlos and Schütze, Oliver and Sun, Jian-Qiao
and Grimme, Christian and Rudolph, Günter and Bischl, Bernd and Trautmann, Heike},
editor={Tantar, Alexandru-Adrian and Tantar, Emilia and Sun, Jian-Qiao and Zhang,
Wei and Ding, Qian and Schütze, Oliver and Emmerich, Michael T M and Legrand,
Pierrick and Del, Moral Pierre and Coello, Coello Carlos A}, year={2014}, pages={115–131},
collection={Advances in Intelligent Systems and Computing} }'
chicago: 'Kerschke, Pascal, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian-Qiao
Sun, Christian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. “Cell
Mapping Techniques for Exploratory Landscape Analysis.” In EVOLVE — A Bridge
between Probability, Set Oriented Numerics, and Evolutionary Computation V,
edited by Alexandru-Adrian Tantar, Emilia Tantar, Jian-Qiao Sun, Wei Zhang, Qian
Ding, Oliver Schütze, Michael T M Emmerich, Pierrick Legrand, Moral Pierre Del,
and Coello Carlos A Coello, 288:115–131. Advances in Intelligent Systems and Computing.
Cham: Springer International Publishing, 2014. https://doi.org/10.1007/978-3-319-07494-8_9.'
ieee: 'P. Kerschke et al., “Cell Mapping Techniques for Exploratory Landscape
Analysis,” in EVOLVE — A Bridge between Probability, Set Oriented Numerics,
and Evolutionary Computation V, vol. 288, A.-A. Tantar, E. Tantar, J.-Q. Sun,
W. Zhang, Q. Ding, O. Schütze, M. T. M. Emmerich, P. Legrand, M. P. Del, and C.
C. A. Coello, Eds. Cham: Springer International Publishing, 2014, pp. 115–131.'
mla: Kerschke, Pascal, et al. “Cell Mapping Techniques for Exploratory Landscape
Analysis.” EVOLVE — A Bridge between Probability, Set Oriented Numerics, and
Evolutionary Computation V, edited by Alexandru-Adrian Tantar et al., vol.
288, Springer International Publishing, 2014, pp. 115–131, doi:10.1007/978-3-319-07494-8_9.
short: 'P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J.-Q. Sun, C. Grimme,
G. Rudolph, B. Bischl, H. Trautmann, in: A.-A. Tantar, E. Tantar, J.-Q. Sun, W.
Zhang, Q. Ding, O. Schütze, M.T.M. Emmerich, P. Legrand, M.P. Del, C.C.A. Coello
(Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V, Springer International Publishing, Cham, 2014, pp. 115–131.'
date_created: 2023-08-04T15:31:52Z
date_updated: 2023-10-16T13:43:42Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-07494-8_9
editor:
- first_name: Alexandru-Adrian
full_name: Tantar, Alexandru-Adrian
last_name: Tantar
- first_name: Emilia
full_name: Tantar, Emilia
last_name: Tantar
- first_name: Jian-Qiao
full_name: Sun, Jian-Qiao
last_name: Sun
- first_name: Wei
full_name: Zhang, Wei
last_name: Zhang
- first_name: Qian
full_name: Ding, Qian
last_name: Ding
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
- first_name: Michael T M
full_name: Emmerich, Michael T M
last_name: Emmerich
- first_name: Pierrick
full_name: Legrand, Pierrick
last_name: Legrand
- first_name: Moral Pierre
full_name: Del, Moral Pierre
last_name: Del
- first_name: Coello Carlos A
full_name: Coello, Coello Carlos A
last_name: Coello
intvolume: ' 288'
language:
- iso: eng
page: 115–131
place: Cham
publication: EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V
publication_identifier:
isbn:
- 978-3-319-07493-1
publisher: Springer International Publishing
series_title: Advances in Intelligent Systems and Computing
status: public
title: Cell Mapping Techniques for Exploratory Landscape Analysis
type: book_chapter
user_id: '15504'
volume: 288
year: '2014'
...
---
_id: '46382'
abstract:
- lang: eng
text: The incorporation of expert knowledge into multiobjective optimization is
an important issue which in this paper is reflected in terms of an aspiration
set consisting of multiple reference points. The behaviour of the recently introduced
evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively
studied for bi-objective optimization problems w.r.t. R-NSGA2 and a respective
variant. It will be shown that the averaged Hausdorff distance, integrated into
AS-EMOA, is an efficient means to accurately approximate the desired aspiration
set.
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: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Schütze O, Grimme C, Trautmann H. A Multiobjective Evolutionary
Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In: Tantar
A, Tantar E, Sun J, et al., eds. EVOLVE — A Bridge between Probability, Set
Oriented Numerics, and Evolutionary Computation V. Vol 288. Advances in Intelligent
Systems and Computing. Springer International Publishing; 2014:261–273. doi:10.1007/978-3-319-07494-8_18'
apa: Rudolph, G., Schütze, O., Grimme, C., & Trautmann, H. (2014). A Multiobjective
Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets.
In A. Tantar, E. Tantar, J. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P.
Legrand, M. P. Del, & C. C. Coello (Eds.), EVOLVE — A Bridge between Probability,
Set Oriented Numerics, and Evolutionary Computation V (Vol. 288, pp. 261–273).
Springer International Publishing. https://doi.org/10.1007/978-3-319-07494-8_18
bibtex: '@inbook{Rudolph_Schütze_Grimme_Trautmann_2014, series={Advances in Intelligent
Systems and Computing}, title={A Multiobjective Evolutionary Algorithm Guided
by Averaged Hausdorff Distance to Aspiration Sets}, volume={288}, DOI={10.1007/978-3-319-07494-8_18},
booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V}, publisher={Springer International Publishing}, author={Rudolph,
G and Schütze, O and Grimme, C and Trautmann, Heike}, editor={Tantar, A and Tantar,
E and Sun, J and Zhang, W and Ding, Q and Schütze, O and Emmerich, M and Legrand,
P and Del, Moral P and Coello, Coello CA}, year={2014}, pages={261–273}, collection={Advances
in Intelligent Systems and Computing} }'
chicago: Rudolph, G, O Schütze, C Grimme, and Heike Trautmann. “A Multiobjective
Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets.”
In EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V, edited by A Tantar, E Tantar, J Sun, W Zhang, Q Ding, O Schütze,
M Emmerich, P Legrand, Moral P Del, and Coello CA Coello, 288:261–273. Advances
in Intelligent Systems and Computing. Springer International Publishing, 2014.
https://doi.org/10.1007/978-3-319-07494-8_18.
ieee: G. Rudolph, O. Schütze, C. Grimme, and H. Trautmann, “A Multiobjective Evolutionary
Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets,” in EVOLVE
— A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation
V, vol. 288, A. Tantar, E. Tantar, J. Sun, W. Zhang, Q. Ding, O. Schütze,
M. Emmerich, P. Legrand, M. P. Del, and C. C. Coello, Eds. Springer International
Publishing, 2014, pp. 261–273.
mla: Rudolph, G., et al. “A Multiobjective Evolutionary Algorithm Guided by Averaged
Hausdorff Distance to Aspiration Sets.” EVOLVE — A Bridge between Probability,
Set Oriented Numerics, and Evolutionary Computation V, edited by A Tantar
et al., vol. 288, Springer International Publishing, 2014, pp. 261–273, doi:10.1007/978-3-319-07494-8_18.
short: 'G. Rudolph, O. Schütze, C. Grimme, H. Trautmann, in: A. Tantar, E. Tantar,
J. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P. Legrand, M.P. Del, C.C.
Coello (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and
Evolutionary Computation V, Springer International Publishing, 2014, pp. 261–273.'
date_created: 2023-08-04T15:33:57Z
date_updated: 2023-10-16T13:43:23Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-07494-8_18
editor:
- first_name: A
full_name: Tantar, A
last_name: Tantar
- first_name: E
full_name: Tantar, E
last_name: Tantar
- first_name: J
full_name: Sun, J
last_name: Sun
- first_name: W
full_name: Zhang, W
last_name: Zhang
- first_name: Q
full_name: Ding, Q
last_name: Ding
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: M
full_name: Emmerich, M
last_name: Emmerich
- first_name: P
full_name: Legrand, P
last_name: Legrand
- first_name: Moral P
full_name: Del, Moral P
last_name: Del
- first_name: Coello CA
full_name: Coello, Coello CA
last_name: Coello
intvolume: ' 288'
language:
- iso: eng
page: 261–273
publication: EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation V
publication_identifier:
isbn:
- 978-3-319-07493-1
publisher: Springer International Publishing
series_title: Advances in Intelligent Systems and Computing
status: public
title: A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance
to Aspiration Sets
type: book_chapter
user_id: '15504'
volume: 288
year: '2014'
...
---
_id: '46383'
abstract:
- lang: eng
text: We propose an evolutionary multiobjective algorithm that approximates multiple
reference points (the aspiration set) in a single run using the concept of the
averaged Hausdorff distance.
author:
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Grimme C, Schütze O, Trautmann H. An Aspiration Set EMOA Based
on Averaged Hausdorff Distances. In: Proceedings of the Learning and Intelligent
OptimizatioN Conference (LION 8). Vol 8426. Lecture Notes in Computer Science.
Springer; 2014:153–156.'
apa: Rudolph, G., Grimme, C., Schütze, O., & Trautmann, H. (2014). An Aspiration
Set EMOA Based on Averaged Hausdorff Distances. Proceedings of the Learning
and Intelligent OptimizatioN Conference (LION 8), 8426, 153–156.
bibtex: '@inproceedings{Rudolph_Grimme_Schütze_Trautmann_2014, place={Gainesville,
Florida, USA}, series={Lecture Notes in Computer Science}, title={An Aspiration
Set EMOA Based on Averaged Hausdorff Distances}, volume={8426}, booktitle={Proceedings
of the Learning and Intelligent OptimizatioN Conference (LION 8)}, publisher={Springer},
author={Rudolph, Günter and Grimme, Christian and Schütze, Oliver and Trautmann,
Heike}, year={2014}, pages={153–156}, collection={Lecture Notes in Computer Science}
}'
chicago: 'Rudolph, Günter, Christian Grimme, Oliver Schütze, and Heike Trautmann.
“An Aspiration Set EMOA Based on Averaged Hausdorff Distances.” In Proceedings
of the Learning and Intelligent OptimizatioN Conference (LION 8), 8426:153–156.
Lecture Notes in Computer Science. Gainesville, Florida, USA: Springer, 2014.'
ieee: G. Rudolph, C. Grimme, O. Schütze, and H. Trautmann, “An Aspiration Set EMOA
Based on Averaged Hausdorff Distances,” in Proceedings of the Learning and
Intelligent OptimizatioN Conference (LION 8), 2014, vol. 8426, pp. 153–156.
mla: Rudolph, Günter, et al. “An Aspiration Set EMOA Based on Averaged Hausdorff
Distances.” Proceedings of the Learning and Intelligent OptimizatioN Conference
(LION 8), vol. 8426, Springer, 2014, pp. 153–156.
short: 'G. Rudolph, C. Grimme, O. Schütze, H. Trautmann, in: Proceedings of the
Learning and Intelligent OptimizatioN Conference (LION 8), Springer, Gainesville,
Florida, USA, 2014, pp. 153–156.'
date_created: 2023-08-04T15:34:44Z
date_updated: 2023-10-16T13:43:59Z
department:
- _id: '34'
- _id: '819'
intvolume: ' 8426'
language:
- iso: eng
page: 153–156
place: Gainesville, Florida, USA
publication: Proceedings of the Learning and Intelligent OptimizatioN Conference (LION
8)
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: An Aspiration Set EMOA Based on Averaged Hausdorff Distances
type: conference
user_id: '15504'
volume: 8426
year: '2014'
...
---
_id: '46384'
abstract:
- lang: eng
text: Multimodal optimization requires maintenance of a good search space coverage
and approximation of several optima at the same time. We analyze two constitutive
optimization algorithms and show that in many cases, a phase transition occurs
at some point, so that either diversity collapses or optimization stagnates. But
how to derive suitable stopping criteria for multimodal optimization? Experimental
results indicate that an algorithm’s population contains sufficient information
to estimate the point in time when several performance indicators reach their
optimum. Thus, stopping criteria are formulated based on summary characteristics
employing objective values and mutation strength.
author:
- first_name: S
full_name: Wessing, S
last_name: Wessing
- first_name: M
full_name: Preuss, M
last_name: Preuss
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Wessing S, Preuss M, Trautmann H. Stopping Criteria for Multimodal Optimization.
In: Bartz-Beielstein T, Branke J, Filipic B, Smith J, eds. Proceedings of the
Parallel Problem Solving from Nature — PPSN XIII. Vol 8672. Lecture Notes
in Computer Science. Springer; 2014:141–150. doi:10.1007/978-3-319-10762-2_14'
apa: Wessing, S., Preuss, M., & Trautmann, H. (2014). Stopping Criteria for
Multimodal Optimization. In T. Bartz-Beielstein, J. Branke, B. Filipic, &
J. Smith (Eds.), Proceedings of the Parallel Problem Solving from Nature —
PPSN XIII (Vol. 8672, pp. 141–150). Springer. https://doi.org/10.1007/978-3-319-10762-2_14
bibtex: '@inproceedings{Wessing_Preuss_Trautmann_2014, place={Ljubljana, Slovenia},
series={Lecture Notes in Computer Science}, title={Stopping Criteria for Multimodal
Optimization}, volume={8672}, DOI={10.1007/978-3-319-10762-2_14},
booktitle={Proceedings of the Parallel Problem Solving from Nature — PPSN XIII},
publisher={Springer}, author={Wessing, S and Preuss, M and Trautmann, Heike},
editor={Bartz-Beielstein, T and Branke, J and Filipic, B and Smith, J}, year={2014},
pages={141–150}, collection={Lecture Notes in Computer Science} }'
chicago: 'Wessing, S, M Preuss, and Heike Trautmann. “Stopping Criteria for Multimodal
Optimization.” In Proceedings of the Parallel Problem Solving from Nature —
PPSN XIII, edited by T Bartz-Beielstein, J Branke, B Filipic, and J Smith,
8672:141–150. Lecture Notes in Computer Science. Ljubljana, Slovenia: Springer,
2014. https://doi.org/10.1007/978-3-319-10762-2_14.'
ieee: 'S. Wessing, M. Preuss, and H. Trautmann, “Stopping Criteria for Multimodal
Optimization,” in Proceedings of the Parallel Problem Solving from Nature —
PPSN XIII, 2014, vol. 8672, pp. 141–150, doi: 10.1007/978-3-319-10762-2_14.'
mla: Wessing, S., et al. “Stopping Criteria for Multimodal Optimization.” Proceedings
of the Parallel Problem Solving from Nature — PPSN XIII, edited by T Bartz-Beielstein
et al., vol. 8672, Springer, 2014, pp. 141–150, doi:10.1007/978-3-319-10762-2_14.
short: 'S. Wessing, M. Preuss, H. Trautmann, in: T. Bartz-Beielstein, J. Branke,
B. Filipic, J. Smith (Eds.), Proceedings of the Parallel Problem Solving from
Nature — PPSN XIII, Springer, Ljubljana, Slovenia, 2014, pp. 141–150.'
date_created: 2023-08-04T15:36:01Z
date_updated: 2023-10-16T13:44:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-10762-2_14
editor:
- first_name: T
full_name: Bartz-Beielstein, T
last_name: Bartz-Beielstein
- first_name: J
full_name: Branke, J
last_name: Branke
- first_name: B
full_name: Filipic, B
last_name: Filipic
- first_name: J
full_name: Smith, J
last_name: Smith
intvolume: ' 8672'
language:
- iso: eng
page: 141–150
place: Ljubljana, Slovenia
publication: Proceedings of the Parallel Problem Solving from Nature — PPSN XIII
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Stopping Criteria for Multimodal Optimization
type: conference
user_id: '15504'
volume: 8672
year: '2014'
...
---
_id: '46385'
abstract:
- lang: eng
text: "In many applications one is faced with the problem that multiple objectives
have to be optimized at the same time. Since typically the solution set of such
multi-objective optimization problems forms a manifold which cannot be computed
analytically, one is in many cases interested in a suitable finite size approximation
of this set. One widely used approach is to find a representative set that maximizes
the dominated hypervolume that is defined by the images in objective space of
these solutions and a given reference point.\r\n\r\nIn this paper, we propose
a new point-wise iterative search procedure, Hypervolume Directed Search (HVDS),
that aims to increase the hypervolume of a given point in an archive for bi-objective
unconstrained optimization problems. We present the HVDS both as a standalone
algorithm and as a local searcher within a specialized evolutionary algorithm.
Numerical results confirm the strength of the novel approach."
author:
- first_name: Hernández V
full_name: Sosa, Hernández V
last_name: Sosa
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Sosa HV, Schütze O, Rudolph G, Trautmann H. The Directed Search Method for
Pareto Front Approximations with Maximum Dominated Hypervolume. In: Emmerich M,
Deutz A, Schuetze O, et al., eds. EVOLVE — A Bridge between Probability, Set
Oriented Numerics, and Evolutionary Computation IV. Vol 227. Advances in Intelligent
Systems and Computing. Springer International Publishing; 2013:189–205. doi:10.1007/978-3-319-01128-8_13'
apa: Sosa, H. V., Schütze, O., Rudolph, G., & Trautmann, H. (2013). The Directed
Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume.
In M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, A. Tantar, P. Moral, P. Legrand,
P. Bouvry, & C. Coello (Eds.), EVOLVE — A Bridge between Probability, Set
Oriented Numerics, and Evolutionary Computation IV (Vol. 227, pp. 189–205).
Springer International Publishing. https://doi.org/10.1007/978-3-319-01128-8_13
bibtex: '@inbook{Sosa_Schütze_Rudolph_Trautmann_2013, series={Advances in Intelligent
Systems and Computing}, title={The Directed Search Method for Pareto Front Approximations
with Maximum Dominated Hypervolume}, volume={227}, DOI={10.1007/978-3-319-01128-8_13},
booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation IV}, publisher={Springer International Publishing}, author={Sosa,
Hernández V and Schütze, O and Rudolph, G and Trautmann, Heike}, editor={Emmerich,
M and Deutz, A and Schuetze, O and Bäck, T and Tantar, A and Moral, PD and Legrand,
P and Bouvry, P and Coello, CA}, year={2013}, pages={189–205}, collection={Advances
in Intelligent Systems and Computing} }'
chicago: Sosa, Hernández V, O Schütze, G Rudolph, and Heike Trautmann. “The Directed
Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume.”
In EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation IV, edited by M Emmerich, A Deutz, O Schuetze, T Bäck, A Tantar,
PD Moral, P Legrand, P Bouvry, and CA Coello, 227:189–205. Advances in Intelligent
Systems and Computing. Springer International Publishing, 2013. https://doi.org/10.1007/978-3-319-01128-8_13.
ieee: H. V. Sosa, O. Schütze, G. Rudolph, and H. Trautmann, “The Directed Search
Method for Pareto Front Approximations with Maximum Dominated Hypervolume,” in
EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation IV, vol. 227, M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, A.
Tantar, P. Moral, P. Legrand, P. Bouvry, and C. Coello, Eds. Springer International
Publishing, 2013, pp. 189–205.
mla: Sosa, Hernández V., et al. “The Directed Search Method for Pareto Front Approximations
with Maximum Dominated Hypervolume.” EVOLVE — A Bridge between Probability,
Set Oriented Numerics, and Evolutionary Computation IV, edited by M Emmerich
et al., vol. 227, Springer International Publishing, 2013, pp. 189–205, doi:10.1007/978-3-319-01128-8_13.
short: 'H.V. Sosa, O. Schütze, G. Rudolph, H. Trautmann, in: M. Emmerich, A. Deutz,
O. Schuetze, T. Bäck, A. Tantar, P. Moral, P. Legrand, P. Bouvry, C. Coello (Eds.),
EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation IV, Springer International Publishing, 2013, pp. 189–205.'
date_created: 2023-08-04T15:37:00Z
date_updated: 2023-10-16T13:44:50Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-01128-8_13
editor:
- first_name: M
full_name: Emmerich, M
last_name: Emmerich
- first_name: A
full_name: Deutz, A
last_name: Deutz
- first_name: O
full_name: Schuetze, O
last_name: Schuetze
- first_name: T
full_name: Bäck, T
last_name: Bäck
- first_name: A
full_name: Tantar, A
last_name: Tantar
- first_name: PD
full_name: Moral, PD
last_name: Moral
- first_name: P
full_name: Legrand, P
last_name: Legrand
- first_name: P
full_name: Bouvry, P
last_name: Bouvry
- first_name: CA
full_name: Coello, CA
last_name: Coello
intvolume: ' 227'
language:
- iso: eng
page: 189–205
publication: EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation IV
publication_identifier:
isbn:
- 978-3-319-01127-1
publisher: Springer International Publishing
series_title: Advances in Intelligent Systems and Computing
status: public
title: The Directed Search Method for Pareto Front Approximations with Maximum Dominated
Hypervolume
type: book_chapter
user_id: '15504'
volume: 227
year: '2013'
...
---
_id: '46386'
abstract:
- lang: eng
text: The averaged Hausdorff distance Δ p is a performance indicator in multi-objective
evolutionary optimization which simultaneously takes into account proximity to
the true Pareto front and uniform spread of solutions. Recently, the multi-objective
evolutionary algorithm Δ p -EMOA was introduced which successfully generates evenly
spaced Pareto front approximations for bi-objective problems by integrating an
external archiving strategy into the SMS-EMOA based on Δ p . In this work a conceptual
generalization of the Δ p -EMOA for higher objective space dimensions is presented
and experimentally compared to state-of-the art EMOA as well as specialized EMOA
variants on three-dimensional optimization problems.
author:
- 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: C
full_name: Dominguez-Medina, C
last_name: Dominguez-Medina
- first_name: O
full_name: Schütze, O
last_name: Schütze
citation:
ama: 'Trautmann H, Rudolph G, Dominguez-Medina C, Schütze O. Finding Evenly Spaced
Pareto Fronts for Three-Objective Optimization Problems. In: Schütze O, Coello
CC, Tantar A, et al., eds. EVOLVE — A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation II. Vol 175. Advances in Intelligent
Systems and Computing. Springer Berlin Heidelberg; 2013:89–105. doi:10.1007/978-3-642-31519-0_6'
apa: Trautmann, H., Rudolph, G., Dominguez-Medina, C., & Schütze, O. (2013).
Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems.
In O. Schütze, C. C. Coello, A. Tantar, E. Tantar, P. Bouvry, M. P. Del, &
P. Legrand (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics,
and Evolutionary Computation II (Vol. 175, pp. 89–105). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-642-31519-0_6
bibtex: '@inbook{Trautmann_Rudolph_Dominguez-Medina_Schütze_2013, series={Advances
in Intelligent Systems and Computing}, title={Finding Evenly Spaced Pareto Fronts
for Three-Objective Optimization Problems}, volume={175}, DOI={10.1007/978-3-642-31519-0_6},
booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation II}, publisher={Springer Berlin Heidelberg}, author={Trautmann, Heike
and Rudolph, G and Dominguez-Medina, C and Schütze, O}, editor={Schütze, O and
Coello, Coello CA and Tantar, A and Tantar, E and Bouvry, P and Del, Moral P and
Legrand, P}, year={2013}, pages={89–105}, collection={Advances in Intelligent
Systems and Computing} }'
chicago: Trautmann, Heike, G Rudolph, C Dominguez-Medina, and O Schütze. “Finding
Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems.” In EVOLVE
— A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation
II, edited by O Schütze, Coello CA Coello, A Tantar, E Tantar, P Bouvry, Moral
P Del, and P Legrand, 175:89–105. Advances in Intelligent Systems and Computing.
Springer Berlin Heidelberg, 2013. https://doi.org/10.1007/978-3-642-31519-0_6.
ieee: H. Trautmann, G. Rudolph, C. Dominguez-Medina, and O. Schütze, “Finding Evenly
Spaced Pareto Fronts for Three-Objective Optimization Problems,” in EVOLVE
— A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation
II, vol. 175, O. Schütze, C. C. Coello, A. Tantar, E. Tantar, P. Bouvry, M.
P. Del, and P. Legrand, Eds. Springer Berlin Heidelberg, 2013, pp. 89–105.
mla: Trautmann, Heike, et al. “Finding Evenly Spaced Pareto Fronts for Three-Objective
Optimization Problems.” EVOLVE — A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation II, edited by O Schütze et al., vol.
175, Springer Berlin Heidelberg, 2013, pp. 89–105, doi:10.1007/978-3-642-31519-0_6.
short: 'H. Trautmann, G. Rudolph, C. Dominguez-Medina, O. Schütze, in: O. Schütze,
C.C. Coello, A. Tantar, E. Tantar, P. Bouvry, M.P. Del, P. Legrand (Eds.), EVOLVE
— A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation
II, Springer Berlin Heidelberg, 2013, pp. 89–105.'
date_created: 2023-08-04T15:38:25Z
date_updated: 2023-10-16T13:45:12Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-642-31519-0_6
editor:
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: Coello CA
full_name: Coello, Coello CA
last_name: Coello
- first_name: A
full_name: Tantar, A
last_name: Tantar
- first_name: E
full_name: Tantar, E
last_name: Tantar
- first_name: P
full_name: Bouvry, P
last_name: Bouvry
- first_name: Moral P
full_name: Del, Moral P
last_name: Del
- first_name: P
full_name: Legrand, P
last_name: Legrand
intvolume: ' 175'
language:
- iso: eng
page: 89–105
publication: EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary
Computation II
publication_identifier:
isbn:
- 978-3-642-31518-3
publisher: Springer Berlin Heidelberg
series_title: Advances in Intelligent Systems and Computing
status: public
title: Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems
type: book_chapter
user_id: '15504'
volume: 175
year: '2013'
...
---
_id: '46388'
abstract:
- lang: eng
text: Understanding the behaviour of well-known algorithms for classical NP-hard
optimisation problems is still a difficult task. With this paper, we contribute
to this research direction and carry out a feature based comparison of local search
and the well-known Christofides approximation algorithm for the Traveling Salesperson
Problem. We use an evolutionary algorithm approach to construct easy and hard
instances for the Christofides algorithm, where we measure hardness in terms of
approximation ratio. Our results point out important features and lead to hard
and easy instances for this famous algorithm. Furthermore, our cross-comparison
gives new insights on the complementary benefits of the different approaches.
author:
- first_name: Samadhi
full_name: Nallaperuma, Samadhi
last_name: Nallaperuma
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H. A Feature-Based
Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem. In: Proceedings of the Twelfth Workshop on Foundations of Genetic
Algorithms XII. FOGA XII ’13. Association for Computing Machinery; 2013:147–160.
doi:10.1145/2460239.2460253'
apa: Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., & Trautmann,
H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm
for the Travelling Salesperson Problem. Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 147–160. https://doi.org/10.1145/2460239.2460253
bibtex: '@inproceedings{Nallaperuma_Wagner_Neumann_Bischl_Mersmann_Trautmann_2013,
place={New York, NY, USA}, series={FOGA XII ’13}, title={A Feature-Based Comparison
of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem}, DOI={10.1145/2460239.2460253},
booktitle={Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII}, publisher={Association for Computing Machinery}, author={Nallaperuma, Samadhi
and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and
Trautmann, Heike}, year={2013}, pages={147–160}, collection={FOGA XII ’13} }'
chicago: 'Nallaperuma, Samadhi, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf
Mersmann, and Heike Trautmann. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” In Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, 147–160.
FOGA XII ’13. New York, NY, USA: Association for Computing Machinery, 2013. https://doi.org/10.1145/2460239.2460253.'
ieee: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann,
“A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem,” in Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 2013, pp. 147–160, doi: 10.1145/2460239.2460253.'
mla: Nallaperuma, Samadhi, et al. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, Association
for Computing Machinery, 2013, pp. 147–160, doi:10.1145/2460239.2460253.
short: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, H. Trautmann,
in: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII,
Association for Computing Machinery, New York, NY, USA, 2013, pp. 147–160.'
date_created: 2023-08-04T15:42:03Z
date_updated: 2023-10-16T13:45:53Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2460239.2460253
keyword:
- approximation algorithms
- local search
- traveling salesperson problem
- feature selection
- prediction
- classification
language:
- iso: eng
page: 147–160
place: New York, NY, USA
publication: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII
publication_identifier:
isbn:
- '9781450319904'
publisher: Association for Computing Machinery
series_title: FOGA XII ’13
status: public
title: A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem
type: conference
user_id: '15504'
year: '2013'
...