---
_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: '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'
...