---
_id: '48863'
abstract:
- lang: eng
text: The novel R package ecr (version 2), short for Evolutionary Computation in
R, provides a comprehensive collection of building blocks for constructing powerful
evolutionary algorithms for single- and multi-objective continuous and combinatorial
optimization problems. It allows to solve standard optimization tasks with few
lines of code using a black-box approach. Moreover, rapid prototyping of non-standard
ideas is possible via an explicit, white-box approach. This paper describes the
design principles of the package and gives some introductory examples on how to
use the package in practise.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Ecr 2.0: A Modular Framework for Evolutionary Computation in R.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
GECCO ’17. Association for Computing Machinery; 2017:1187–1193. doi:10.1145/3067695.3082470'
apa: 'Bossek, J. (2017). Ecr 2.0: A Modular Framework for Evolutionary Computation
in R. Proceedings of the Genetic and Evolutionary Computation Conference Companion,
1187–1193. https://doi.org/10.1145/3067695.3082470'
bibtex: '@inproceedings{Bossek_2017, place={New York, NY, USA}, series={GECCO ’17},
title={Ecr 2.0: A Modular Framework for Evolutionary Computation in R}, DOI={10.1145/3067695.3082470}, booktitle={Proceedings
of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association
for Computing Machinery}, author={Bossek, Jakob}, year={2017}, pages={1187–1193},
collection={GECCO ’17} }'
chicago: 'Bossek, Jakob. “Ecr 2.0: A Modular Framework for Evolutionary Computation
in R.” In Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1187–1193. GECCO ’17. New York, NY, USA: Association for Computing
Machinery, 2017. https://doi.org/10.1145/3067695.3082470.'
ieee: 'J. Bossek, “Ecr 2.0: A Modular Framework for Evolutionary Computation in
R,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion,
2017, pp. 1187–1193, doi: 10.1145/3067695.3082470.'
mla: 'Bossek, Jakob. “Ecr 2.0: A Modular Framework for Evolutionary Computation
in R.” Proceedings of the Genetic and Evolutionary Computation Conference Companion,
Association for Computing Machinery, 2017, pp. 1187–1193, doi:10.1145/3067695.3082470.'
short: 'J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference
Companion, Association for Computing Machinery, New York, NY, USA, 2017, pp. 1187–1193.'
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:45:05Z
department:
- _id: '819'
doi: 10.1145/3067695.3082470
extern: '1'
keyword:
- evolutionary optimization
- software-tools
language:
- iso: eng
page: 1187–1193
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-4939-0
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’17
status: public
title: 'Ecr 2.0: A Modular Framework for Evolutionary Computation in R'
type: conference
user_id: '102979'
year: '2017'
...
---
_id: '48857'
abstract:
- lang: eng
text: 'While finding minimum-cost spanning trees (MST) in undirected graphs is solvable
in polynomial time, the multi-criteria minimum spanning tree problem (mcMST) is
NP-hard. Interestingly, the mcMST problem has not been in focus of evolutionary
computation research for a long period of time, although, its relevance for real
world problems is easy to see. The available and most notable approaches by Zhou
and Gen as well as by Knowles and Corne concentrate on solution encoding and on
fairly dated selection mechanisms. In this work, we revisit the mcMST and focus
on the mutation operators as exploratory components of evolutionary algorithms
neglected so far. We investigate optimal solution characteristics to discuss current
mutation strategies, identify shortcomings of these operators, and propose a sub-tree
based operator which offers what we term Pareto-beneficial behavior: ensuring
convergence and diversity at the same time. The operator is empirically evaluated
inside modern standard evolutionary meta-heuristics for multi-criteria optimization
and compared to hitherto applied mutation operators in the context of mcMST.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Bossek J, Grimme C. A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria
Minimum Spanning Tree Problem. In: 2017 IEEE Symposium Series on Computational
Intelligence (SSCI). ; 2017:1–8. doi:10.1109/SSCI.2017.8285183'
apa: Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem. 2017 IEEE Symposium Series
on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285183
bibtex: '@inproceedings{Bossek_Grimme_2017, title={A Pareto-Beneficial Sub-Tree
Mutation for the Multi-Criteria Minimum Spanning Tree Problem}, DOI={10.1109/SSCI.2017.8285183},
booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Bossek,
Jakob and Grimme, Christian}, year={2017}, pages={1–8} }'
chicago: Bossek, Jakob, and Christian Grimme. “A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem.” In 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8, 2017. https://doi.org/10.1109/SSCI.2017.8285183.
ieee: 'J. Bossek and C. Grimme, “A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria
Minimum Spanning Tree Problem,” in 2017 IEEE Symposium Series on Computational
Intelligence (SSCI), 2017, pp. 1–8, doi: 10.1109/SSCI.2017.8285183.'
mla: Bossek, Jakob, and Christian Grimme. “A Pareto-Beneficial Sub-Tree Mutation
for the Multi-Criteria Minimum Spanning Tree Problem.” 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285183.
short: 'J. Bossek, C. Grimme, in: 2017 IEEE Symposium Series on Computational Intelligence
(SSCI), 2017, pp. 1–8.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:28Z
department:
- _id: '819'
doi: 10.1109/SSCI.2017.8285183
extern: '1'
keyword:
- Convergence
- Encoding
- Euclidean distance
- Evolutionary computation
- Heating systems
- Optimization
- Standards
language:
- iso: eng
page: 1–8
publication: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
status: public
title: A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning
Tree Problem
type: conference
user_id: '102979'
year: '2017'
...
---
_id: '48856'
abstract:
- lang: eng
text: There exist many optimal or heuristic priority rules for machine scheduling
problems, which can easily be integrated into single-objective evolutionary algorithms
via mutation operators. However, in the multi-objective case, simultaneously applying
different priorities for different objectives may cause severe disruptions in
the genome and may lead to inferior solutions. In this paper, we combine an existing
mutation operator concept with new insights from detailed observation of the structure
of solutions for multi-objective machine scheduling problems. This allows the
comprehensive integration of priority rules to produce better Pareto-front approximations.
We evaluate the extended operator concept compared to standard swap mutation and
the stand-alone components of our hybrid scheme, which performs best in all evaluated
cases.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Bossek J, Grimme C. An Extended Mutation-Based Priority-Rule Integration Concept
for Multi-Objective Machine Scheduling. In: 2017 IEEE Symposium Series on Computational
Intelligence (SSCI). ; 2017:1–8. doi:10.1109/SSCI.2017.8285224'
apa: Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling. 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285224
bibtex: '@inproceedings{Bossek_Grimme_2017, title={An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling}, DOI={10.1109/SSCI.2017.8285224},
booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Bossek,
Jakob and Grimme, Christian}, year={2017}, pages={1–8} }'
chicago: Bossek, Jakob, and Christian Grimme. “An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling.” In 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 1–8, 2017. https://doi.org/10.1109/SSCI.2017.8285224.
ieee: 'J. Bossek and C. Grimme, “An Extended Mutation-Based Priority-Rule Integration
Concept for Multi-Objective Machine Scheduling,” in 2017 IEEE Symposium Series
on Computational Intelligence (SSCI), 2017, pp. 1–8, doi: 10.1109/SSCI.2017.8285224.'
mla: Bossek, Jakob, and Christian Grimme. “An Extended Mutation-Based Priority-Rule
Integration Concept for Multi-Objective Machine Scheduling.” 2017 IEEE Symposium
Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285224.
short: 'J. Bossek, C. Grimme, in: 2017 IEEE Symposium Series on Computational Intelligence
(SSCI), 2017, pp. 1–8.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:36Z
department:
- _id: '819'
doi: 10.1109/SSCI.2017.8285224
extern: '1'
keyword:
- Evolutionary computation
- Processor scheduling
- Schedules
- Scheduling
- Sociology
- Standards
- Statistics
language:
- iso: eng
page: 1–8
publication: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
publication_status: published
status: public
title: An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective
Machine Scheduling
type: conference
user_id: '102979'
year: '2017'
...
---
_id: '48864'
abstract:
- lang: eng
text: 'Bossek, (2017), mcMST: A Toolbox for the Multi-Criteria Minimum Spanning
Tree Problem, Journal of Open Source Software, 2(17), 374, doi:10.21105/joss.00374'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem.
Journal of Open Source Software. 2017;2(17):374. doi:10.21105/joss.00374'
apa: 'Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning
Tree Problem. Journal of Open Source Software, 2(17), 374. https://doi.org/10.21105/joss.00374'
bibtex: '@article{Bossek_2017, title={mcMST: A Toolbox for the Multi-Criteria Minimum
Spanning Tree Problem}, volume={2}, DOI={10.21105/joss.00374},
number={17}, journal={Journal of Open Source Software}, author={Bossek, Jakob},
year={2017}, pages={374} }'
chicago: 'Bossek, Jakob. “McMST: A Toolbox for the Multi-Criteria Minimum Spanning
Tree Problem.” Journal of Open Source Software 2, no. 17 (2017): 374. https://doi.org/10.21105/joss.00374.'
ieee: 'J. Bossek, “mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree
Problem,” Journal of Open Source Software, vol. 2, no. 17, p. 374, 2017,
doi: 10.21105/joss.00374.'
mla: 'Bossek, Jakob. “McMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree
Problem.” Journal of Open Source Software, vol. 2, no. 17, 2017, p. 374,
doi:10.21105/joss.00374.'
short: J. Bossek, Journal of Open Source Software 2 (2017) 374.
date_created: 2023-11-14T15:58:55Z
date_updated: 2023-12-13T10:52:04Z
department:
- _id: '819'
doi: 10.21105/joss.00374
intvolume: ' 2'
issue: '17'
language:
- iso: eng
page: '374'
publication: Journal of Open Source Software
publication_identifier:
issn:
- 2475-9066
status: public
title: 'mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem'
type: journal_article
user_id: '102979'
volume: 2
year: '2017'
...
---
_id: '48865'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Smoof: Single- and Multi-Objective Optimization Test Functions.
The R Journal. 2017;9(1):103–113.'
apa: 'Bossek, J. (2017). Smoof: Single- and Multi-Objective Optimization Test Functions.
The R Journal, 9(1), 103–113.'
bibtex: '@article{Bossek_2017, title={Smoof: Single- and Multi-Objective Optimization
Test Functions}, volume={9}, number={1}, journal={The R Journal}, author={Bossek,
Jakob}, year={2017}, pages={103–113} }'
chicago: 'Bossek, Jakob. “Smoof: Single- and Multi-Objective Optimization Test Functions.”
The R Journal 9, no. 1 (2017): 103–113.'
ieee: 'J. Bossek, “Smoof: Single- and Multi-Objective Optimization Test Functions,”
The R Journal, vol. 9, no. 1, pp. 103–113, 2017.'
mla: 'Bossek, Jakob. “Smoof: Single- and Multi-Objective Optimization Test Functions.”
The R Journal, vol. 9, no. 1, 2017, pp. 103–113.'
short: J. Bossek, The R Journal 9 (2017) 103–113.
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:51:57Z
department:
- _id: '819'
intvolume: ' 9'
issue: '1'
language:
- iso: eng
page: 103–113
publication: The R Journal
publication_identifier:
issn:
- 2073-4859
status: public
title: 'Smoof: Single- and Multi-Objective Optimization Test Functions'
type: journal_article
user_id: '102979'
volume: 9
year: '2017'
...
---
_id: '48837'
author:
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Jakob
full_name: Richter, Jakob
last_name: Richter
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Daniel
full_name: Horn, Daniel
last_name: Horn
- first_name: Janek
full_name: Thomas, Janek
last_name: Thomas
- first_name: Michel
full_name: Lang, Michel
last_name: Lang
citation:
ama: 'Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M. mlrMBO: A Modular
Framework for Model-Based Optimization of Expensive Black-Box Functions. CoRR.
Published online 2017.'
apa: 'Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M.
(2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive
Black-Box Functions. CoRR.'
bibtex: '@article{Bischl_Richter_Bossek_Horn_Thomas_Lang_2017, title={mlrMBO: A
Modular Framework for Model-Based Optimization of Expensive Black-Box Functions},
journal={CoRR}, author={Bischl, Bernd and Richter, Jakob and Bossek, Jakob and
Horn, Daniel and Thomas, Janek and Lang, Michel}, year={2017} }'
chicago: 'Bischl, Bernd, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas,
and Michel Lang. “MlrMBO: A Modular Framework for Model-Based Optimization of
Expensive Black-Box Functions.” CoRR, 2017.'
ieee: 'B. Bischl, J. Richter, J. Bossek, D. Horn, J. Thomas, and M. Lang, “mlrMBO:
A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions,”
CoRR, 2017.'
mla: 'Bischl, Bernd, et al. “MlrMBO: A Modular Framework for Model-Based Optimization
of Expensive Black-Box Functions.” CoRR, 2017.'
short: B. Bischl, J. Richter, J. Bossek, D. Horn, J. Thomas, M. Lang, CoRR (2017).
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:52:31Z
department:
- _id: '819'
language:
- iso: eng
publication: CoRR
status: public
title: 'mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box
Functions'
type: journal_article
user_id: '102979'
year: '2017'
...
---
_id: '46364'
abstract:
- lang: eng
text: Automated algorithm configuration procedures play an increasingly important
role in the development and application of algorithms for a wide range of computationally
challenging problems. Until very recently, these configuration procedures were
limited to optimising a single performance objective, such as the running time
or solution quality achieved by the algorithm being configured. However, in many
applications there is more than one performance objective of interest. This gives
rise to the multi-objective automatic algorithm configuration problem, which involves
finding a Pareto set of configurations of a given target algorithm that characterises
trade-offs between multiple performance objectives. In this work, we introduce
MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective
algorithm configuration framework ParamILS, and demonstrate that it produces good
results on several challenging bi-objective algorithm configuration scenarios
compared to a base-line obtained from using a state-of-the-art single-objective
algorithm configurator.
author:
- first_name: A
full_name: Blot, A
last_name: Blot
- first_name: H
full_name: Hoos, H
last_name: Hoos
- first_name: L
full_name: Jourdan, L
last_name: Jourdan
- first_name: M
full_name: Marmion, M
last_name: Marmion
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Blot A, Hoos H, Jourdan L, Marmion M, Trautmann H. MO-ParamILS: A Multi-objective
Automatic Algorithm Configuration Framework. In: et al. Joaquin V, ed. LION
2016: Learning and Intelligent Optimization. Vol 10079. LNTCS. Springer International
Publishing; 2016:32–47. doi:10.1007/978-3-319-50349-3_3'
apa: 'Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016).
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In
V. et al. Joaquin (Ed.), LION 2016: Learning and Intelligent Optimization
(Vol. 10079, pp. 32–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_3'
bibtex: '@inproceedings{Blot_Hoos_Jourdan_Marmion_Trautmann_2016, place={Cham},
series={LNTCS}, title={MO-ParamILS: A Multi-objective Automatic Algorithm Configuration
Framework}, volume={10079}, DOI={10.1007/978-3-319-50349-3_3},
booktitle={LION 2016: Learning and Intelligent Optimization}, publisher={Springer
International Publishing}, author={Blot, A and Hoos, H and Jourdan, L and Marmion,
M and Trautmann, Heike}, editor={et al. Joaquin, Vanschooren}, year={2016}, pages={32–47},
collection={LNTCS} }'
chicago: 'Blot, A, H Hoos, L Jourdan, M Marmion, and Heike Trautmann. “MO-ParamILS:
A Multi-Objective Automatic Algorithm Configuration Framework.” In LION 2016:
Learning and Intelligent Optimization, edited by Vanschooren et al. Joaquin,
10079:32–47. LNTCS. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_3.'
ieee: 'A. Blot, H. Hoos, L. Jourdan, M. Marmion, and H. Trautmann, “MO-ParamILS:
A Multi-objective Automatic Algorithm Configuration Framework,” in LION 2016:
Learning and Intelligent Optimization, 2016, vol. 10079, pp. 32–47, doi: 10.1007/978-3-319-50349-3_3.'
mla: 'Blot, A., et al. “MO-ParamILS: A Multi-Objective Automatic Algorithm Configuration
Framework.” LION 2016: Learning and Intelligent Optimization, edited by
Vanschooren et al. Joaquin, vol. 10079, Springer International Publishing, 2016,
pp. 32–47, doi:10.1007/978-3-319-50349-3_3.'
short: 'A. Blot, H. Hoos, L. Jourdan, M. Marmion, H. Trautmann, in: V. et al. Joaquin
(Ed.), LION 2016: Learning and Intelligent Optimization, Springer International
Publishing, Cham, 2016, pp. 32–47.'
date_created: 2023-08-04T15:10:09Z
date_updated: 2023-10-16T13:37:50Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-50349-3_3
editor:
- first_name: Vanschooren
full_name: et al. Joaquin, Vanschooren
last_name: et al. Joaquin
intvolume: ' 10079'
language:
- iso: eng
page: 32–47
place: Cham
publication: 'LION 2016: Learning and Intelligent Optimization'
publisher: Springer International Publishing
series_title: LNTCS
status: public
title: 'MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework'
type: conference
user_id: '15504'
volume: 10079
year: '2016'
...
---
_id: '46363'
abstract:
- lang: eng
text: "The averaged Hausdorff distance has been proposed as an indicator for assessing
the quality of finitely sized approximations of the Pareto front of a multiobjective
problem. Since many set-based, iterative optimization algorithms store their currently
best approximation in an internal archive these approximations are also termed
archives. In case of two objectives and continuous variables it is known that
the best approximations in terms of averaged Hausdorff distance are subsets of
the Pareto front if it is concave. If it is linear or circularly concave the points
of the best approximation are equally spaced.\r\n\r\nHere, it is proven that the
optimal averaged Hausdorff approximation and the Pareto front have an empty intersection
if the Pareto front is circularly convex. But the points of the best approximation
are equally spaced and they rapidly approach the Pareto front for increasing size
of the approximation."
author:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Schütze O, Trautmann H. On the Closest Averaged Hausdorff Archive
for a Circularly Convex Pareto Front. In: Squillero G, Burelli P, eds. Applications
of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016,
Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II. Springer
International Publishing; 2016:42–55. doi:10.1007/978-3-319-31153-1_4'
apa: 'Rudolph, G., Schütze, O., & Trautmann, H. (2016). On the Closest Averaged
Hausdorff Archive for a Circularly Convex Pareto Front. In G. Squillero &
P. Burelli (Eds.), Applications of Evolutionary Computation: 19$^th$ European
Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II (pp. 42–55). Springer International Publishing. https://doi.org/10.1007/978-3-319-31153-1_4'
bibtex: '@inbook{Rudolph_Schütze_Trautmann_2016, place={Cham}, title={On the Closest
Averaged Hausdorff Archive for a Circularly Convex Pareto Front}, DOI={10.1007/978-3-319-31153-1_4},
booktitle={Applications of Evolutionary Computation: 19$^th$ European Conference,
EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II}, publisher={Springer International Publishing}, author={Rudolph, G and
Schütze, O and Trautmann, Heike}, editor={Squillero, G and Burelli, P}, year={2016},
pages={42–55} }'
chicago: 'Rudolph, G, O Schütze, and Heike Trautmann. “On the Closest Averaged Hausdorff
Archive for a Circularly Convex Pareto Front.” In Applications of Evolutionary
Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal,
March 30 — April 1, 2016, Proceedings, Part II, edited by G Squillero and
P Burelli, 42–55. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-31153-1_4.'
ieee: 'G. Rudolph, O. Schütze, and H. Trautmann, “On the Closest Averaged Hausdorff
Archive for a Circularly Convex Pareto Front,” in Applications of Evolutionary
Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal,
March 30 — April 1, 2016, Proceedings, Part II, G. Squillero and P. Burelli,
Eds. Cham: Springer International Publishing, 2016, pp. 42–55.'
mla: 'Rudolph, G., et al. “On the Closest Averaged Hausdorff Archive for a Circularly
Convex Pareto Front.” Applications of Evolutionary Computation: 19$^th$ European
Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings,
Part II, edited by G Squillero and P Burelli, Springer International Publishing,
2016, pp. 42–55, doi:10.1007/978-3-319-31153-1_4.'
short: 'G. Rudolph, O. Schütze, H. Trautmann, in: G. Squillero, P. Burelli (Eds.),
Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications
2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II, Springer
International Publishing, Cham, 2016, pp. 42–55.'
date_created: 2023-08-04T15:09:14Z
date_updated: 2023-10-16T13:37:33Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-31153-1_4
editor:
- first_name: G
full_name: Squillero, G
last_name: Squillero
- first_name: P
full_name: Burelli, P
last_name: Burelli
language:
- iso: eng
page: 42–55
place: Cham
publication: 'Applications of Evolutionary Computation: 19$^th$ European Conference,
EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part
II'
publication_identifier:
isbn:
- 978-3-319-31153-1
publisher: Springer International Publishing
status: public
title: On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front
type: book_chapter
user_id: '15504'
year: '2016'
...
---
_id: '46369'
abstract:
- lang: eng
text: This paper formally defines multimodality in multiobjective optimization (MO).
We introduce a test-bed in which multimodal MO problems with known properties
can be constructed as well as numerical characteristics of the resulting landscape.
Gradient- and local search based strategies are compared on exemplary problems
together with specific performance indicators in the multimodal MO setting. By
this means the foundation for Exploratory Landscape Analysis in MO is provided.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: André
full_name: Deutz, André
last_name: Deutz
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Michael
full_name: Emmerich, Michael
last_name: Emmerich
citation:
ama: 'Kerschke P, Wang H, Preuss M, et al. Towards Analyzing Multimodality of Multiobjective
Landscapes. In: Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV). Lecture Notes in Computer Science.
Springer; 2016:962–972. doi:10.1007/978-3-319-45823-6_90'
apa: Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., &
Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes.
Proceedings of the 14$^th$ International Conference on Parallel Problem Solving
from Nature (PPSN XIV), 962–972. https://doi.org/10.1007/978-3-319-45823-6_90
bibtex: '@inproceedings{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2016,
place={Edinburgh, Scotland}, series={Lecture Notes in Computer Science}, title={Towards
Analyzing Multimodality of Multiobjective Landscapes}, DOI={10.1007/978-3-319-45823-6_90},
booktitle={Proceedings of the 14$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XIV)}, publisher={Springer}, author={Kerschke, Pascal
and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann,
Heike and Emmerich, Michael}, year={2016}, pages={962–972}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz,
Heike Trautmann, and Michael Emmerich. “Towards Analyzing Multimodality of Multiobjective
Landscapes.” In Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), 962–972. Lecture Notes in Computer
Science. Edinburgh, Scotland: Springer, 2016. https://doi.org/10.1007/978-3-319-45823-6_90.'
ieee: 'P. Kerschke et al., “Towards Analyzing Multimodality of Multiobjective
Landscapes,” in Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), 2016, pp. 962–972, doi: 10.1007/978-3-319-45823-6_90.'
mla: Kerschke, Pascal, et al. “Towards Analyzing Multimodality of Multiobjective
Landscapes.” Proceedings of the 14$^th$ International Conference on Parallel
Problem Solving from Nature (PPSN XIV), Springer, 2016, pp. 962–972, doi:10.1007/978-3-319-45823-6_90.
short: 'P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich,
in: Proceedings of the 14$^th$ International Conference on Parallel Problem Solving
from Nature (PPSN XIV), Springer, Edinburgh, Scotland, 2016, pp. 962–972.'
date_created: 2023-08-04T15:16:02Z
date_updated: 2023-10-16T13:39:42Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-319-45823-6_90
language:
- iso: eng
page: 962–972
place: Edinburgh, Scotland
publication: Proceedings of the 14$^th$ International Conference on Parallel Problem
Solving from Nature (PPSN XIV)
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Towards Analyzing Multimodality of Multiobjective Landscapes
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '46367'
abstract:
- lang: eng
text: When selecting the best suited algorithm for an unknown optimization problem,
it is useful to possess some a priori knowledge of the problem at hand. In the
context of single-objective, continuous optimization problems such knowledge can
be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically
identifies properties of a landscape, e.g., the so-called funnel structures, based
on an initial sample. In this paper, we extract the relevant features (for detecting
funnels) out of a large set of landscape features when only given a small initial
sample consisting of 50 x D observations, where D is the number of decision space
dimensions. This is already in the range of the start population sizes of many
evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used
for training the classifier, and the approach is then very successfully validated
on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching
competition problems.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Mike
full_name: Preuss, Mike
last_name: Preuss
- first_name: Simon
full_name: Wessing, Simon
last_name: Wessing
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Preuss M, Wessing S, Trautmann H. Low-Budget Exploratory Landscape
Analysis on Multiple Peaks Models. In: Proceedings of the 18$^th$ Annual Conference
on Genetic and Evolutionary Computation. ; 2016:229–236. doi:10.1145/2908812.2908845'
apa: Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget
Exploratory Landscape Analysis on Multiple Peaks Models. Proceedings of the
18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236.
https://doi.org/10.1145/2908812.2908845
bibtex: '@inproceedings{Kerschke_Preuss_Wessing_Trautmann_2016, place={Denver, CO,
USA}, title={Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models},
DOI={10.1145/2908812.2908845},
booktitle={Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary
Computation}, author={Kerschke, Pascal and Preuss, Mike and Wessing, Simon and
Trautmann, Heike}, year={2016}, pages={229–236} }'
chicago: Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. “Low-Budget
Exploratory Landscape Analysis on Multiple Peaks Models.” In Proceedings of
the 18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236.
Denver, CO, USA, 2016. https://doi.org/10.1145/2908812.2908845.
ieee: 'P. Kerschke, M. Preuss, S. Wessing, and H. Trautmann, “Low-Budget Exploratory
Landscape Analysis on Multiple Peaks Models,” in Proceedings of the 18$^th$
Annual Conference on Genetic and Evolutionary Computation, 2016, pp. 229–236,
doi: 10.1145/2908812.2908845.'
mla: Kerschke, Pascal, et al. “Low-Budget Exploratory Landscape Analysis on Multiple
Peaks Models.” Proceedings of the 18$^th$ Annual Conference on Genetic and
Evolutionary Computation, 2016, pp. 229–236, doi:10.1145/2908812.2908845.
short: 'P. Kerschke, M. Preuss, S. Wessing, H. Trautmann, in: Proceedings of the
18$^th$ Annual Conference on Genetic and Evolutionary Computation, Denver, CO,
USA, 2016, pp. 229–236.'
date_created: 2023-08-04T15:14:06Z
date_updated: 2023-10-16T13:38:47Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2908812.2908845
language:
- iso: eng
page: 229–236
place: Denver, CO, USA
publication: Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- 978-1-4503-4206-3
status: public
title: Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '46371'
abstract:
- lang: eng
text: "One main task in evolutionary multiobjective optimization (EMO) is to obtain
a suitable finite size approximation of the Pareto front which is the image of
the solution set, termed the Pareto set, of a given multiobjective optimization
problem. In the technical literature, the characteristic of the desired approximation
is commonly expressed by closeness to the Pareto front and a sufficient spread
of the solutions obtained. In this paper, we first make an effort to show by theoretical
and empirical findings that the recently proposed Averaged Hausdorff (or Δ\U0001D45D-)
indicator indeed aims at fulfilling both performance criteria for bi-objective
optimization problems. In the second part of this paper, standard EMO algorithms
combined with a specialized archiver and a postprocessing step based on the Δ\U0001D45D
indicator are introduced which sufficiently approximate the Δ\U0001D45D-optimal
archives and generate solutions evenly spread along the Pareto front."
author:
- first_name: G
full_name: Rudolph, G
last_name: Rudolph
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: C
full_name: Grimme, C
last_name: Grimme
- first_name: C
full_name: Domínguez-Medina, C
last_name: Domínguez-Medina
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Rudolph G, Schütze O, Grimme C, Domínguez-Medina C, Trautmann H. Optimal averaged
Hausdorff archives for bi-objective problems: theoretical and numerical results.
Computational Optimization and Applications (Comput Optim Appl). 2016;64(2):589–618.
doi:10.1007/s10589-015-9815-8'
apa: 'Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann,
H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical
and numerical results. Computational Optimization and Applications (Comput.
Optim. Appl.), 64(2), 589–618. https://doi.org/10.1007/s10589-015-9815-8'
bibtex: '@article{Rudolph_Schütze_Grimme_Domínguez-Medina_Trautmann_2016, title={Optimal
averaged Hausdorff archives for bi-objective problems: theoretical and numerical
results}, volume={64}, DOI={10.1007/s10589-015-9815-8},
number={2}, journal={Computational Optimization and Applications (Comput. Optim.
Appl.)}, author={Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina,
C and Trautmann, Heike}, year={2016}, pages={589–618} }'
chicago: 'Rudolph, G, O Schütze, C Grimme, C Domínguez-Medina, and Heike Trautmann.
“Optimal Averaged Hausdorff Archives for Bi-Objective Problems: Theoretical and
Numerical Results.” Computational Optimization and Applications (Comput. Optim.
Appl.) 64, no. 2 (2016): 589–618. https://doi.org/10.1007/s10589-015-9815-8.'
ieee: 'G. Rudolph, O. Schütze, C. Grimme, C. Domínguez-Medina, and H. Trautmann,
“Optimal averaged Hausdorff archives for bi-objective problems: theoretical and
numerical results,” Computational Optimization and Applications (Comput. Optim.
Appl.), vol. 64, no. 2, pp. 589–618, 2016, doi: 10.1007/s10589-015-9815-8.'
mla: 'Rudolph, G., et al. “Optimal Averaged Hausdorff Archives for Bi-Objective
Problems: Theoretical and Numerical Results.” Computational Optimization and
Applications (Comput. Optim. Appl.), vol. 64, no. 2, 2016, pp. 589–618, doi:10.1007/s10589-015-9815-8.'
short: G. Rudolph, O. Schütze, C. Grimme, C. Domínguez-Medina, H. Trautmann, Computational
Optimization and Applications (Comput. Optim. Appl.) 64 (2016) 589–618.
date_created: 2023-08-04T15:17:48Z
date_updated: 2023-10-16T13:40:21Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/s10589-015-9815-8
intvolume: ' 64'
issue: '2'
language:
- iso: eng
page: 589–618
publication: Computational Optimization and Applications (Comput. Optim. Appl.)
status: public
title: 'Optimal averaged Hausdorff archives for bi-objective problems: theoretical
and numerical results'
type: journal_article
user_id: '15504'
volume: 64
year: '2016'
...
---
_id: '46372'
abstract:
- lang: eng
text: We present a new hybrid evolutionary algorithm for the effective hypervolume
approximation of the Pareto front of a given differentiable multi-objective optimization
problem. Starting point for the local search (LS) mechanism is a new division
of the decision space as we will argue that in each of these regions a different
LS strategy seems to be most promising. For the LS in two out of the three regions
we will utilize and adapt the Directed Search method which is capable of steering
the search into any direction given in objective space and which is thus well
suited for the problem at hand. We further on integrate the resulting LS mechanism
into SMS-EMOA, a state-of-the-art evolutionary algorithm for hypervolume approximations.
Finally, we will present some numerical results on several benchmark problems
with two and three objectives indicating the strength and competitiveness of the
novel hybrid.
author:
- first_name: O
full_name: Schütze, O
last_name: Schütze
- first_name: Hernandez VA
full_name: Sosa, Hernandez VA
last_name: Sosa
- 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
citation:
ama: Schütze O, Sosa HV, Trautmann H, Rudolph G. The Hypervolume based Directed
Search Method for Multi-Objective Optimization Problems. Journal of Heuristics.
2016;22(3):273–300. doi:10.1007/s10732-016-9310-0
apa: Schütze, O., Sosa, H. V., Trautmann, H., & Rudolph, G. (2016). The Hypervolume
based Directed Search Method for Multi-Objective Optimization Problems. Journal
of Heuristics, 22(3), 273–300. https://doi.org/10.1007/s10732-016-9310-0
bibtex: '@article{Schütze_Sosa_Trautmann_Rudolph_2016, title={The Hypervolume based
Directed Search Method for Multi-Objective Optimization Problems}, volume={22},
DOI={10.1007/s10732-016-9310-0},
number={3}, journal={Journal of Heuristics}, author={Schütze, O and Sosa, Hernandez
VA and Trautmann, Heike and Rudolph, G}, year={2016}, pages={273–300} }'
chicago: 'Schütze, O, Hernandez VA Sosa, Heike Trautmann, and G Rudolph. “The Hypervolume
Based Directed Search Method for Multi-Objective Optimization Problems.” Journal
of Heuristics 22, no. 3 (2016): 273–300. https://doi.org/10.1007/s10732-016-9310-0.'
ieee: 'O. Schütze, H. V. Sosa, H. Trautmann, and G. Rudolph, “The Hypervolume based
Directed Search Method for Multi-Objective Optimization Problems,” Journal
of Heuristics, vol. 22, no. 3, pp. 273–300, 2016, doi: 10.1007/s10732-016-9310-0.'
mla: Schütze, O., et al. “The Hypervolume Based Directed Search Method for Multi-Objective
Optimization Problems.” Journal of Heuristics, vol. 22, no. 3, 2016, pp.
273–300, doi:10.1007/s10732-016-9310-0.
short: O. Schütze, H.V. Sosa, H. Trautmann, G. Rudolph, Journal of Heuristics 22
(2016) 273–300.
date_created: 2023-08-04T15:19:11Z
date_updated: 2023-10-16T13:40:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/s10732-016-9310-0
intvolume: ' 22'
issue: '3'
language:
- iso: eng
page: 273–300
publication: Journal of Heuristics
status: public
title: The Hypervolume based Directed Search Method for Multi-Objective Optimization
Problems
type: journal_article
user_id: '15504'
volume: 22
year: '2016'
...
---
_id: '46368'
abstract:
- lang: eng
text: Exploratory Landscape Analysis (ELA) aims at understanding characteristics
of single-objective continuous (black-box) optimization problems in an automated
way. Moreover, the approach provides the basis for constructing algorithm selection
models for unseen problem instances. Recently, it has gained increasing attention
and numerical features have been designed by various research groups. This paper
introduces the R-Package FLACCO which makes all relevant features available in
a unified framework together with efficient helper functions. Moreover, a case
study which gives perspectives to ELA for multi-objective optimization problems
is presented.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Trautmann H. The R-Package FLACCO for Exploratory Landscape Analysis
with Applications to Multi-Objective Optimization Problems. In: Proceedings
of the IEEE Congress on Evolutionary Computation (CEC). ; 2016. doi:10.1109/CEC.2016.7748359'
apa: Kerschke, P., & Trautmann, H. (2016). The R-Package FLACCO for Exploratory
Landscape Analysis with Applications to Multi-Objective Optimization Problems.
Proceedings of the IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2016.7748359
bibtex: '@inproceedings{Kerschke_Trautmann_2016, place={Vancouver, BC, Kanada},
title={The R-Package FLACCO for Exploratory Landscape Analysis with Applications
to Multi-Objective Optimization Problems}, DOI={10.1109/CEC.2016.7748359},
booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
author={Kerschke, Pascal and Trautmann, Heike}, year={2016} }'
chicago: Kerschke, Pascal, and Heike Trautmann. “The R-Package FLACCO for Exploratory
Landscape Analysis with Applications to Multi-Objective Optimization Problems.”
In Proceedings of the IEEE Congress on Evolutionary Computation (CEC).
Vancouver, BC, Kanada, 2016. https://doi.org/10.1109/CEC.2016.7748359.
ieee: 'P. Kerschke and H. Trautmann, “The R-Package FLACCO for Exploratory Landscape
Analysis with Applications to Multi-Objective Optimization Problems,” 2016, doi:
10.1109/CEC.2016.7748359.'
mla: Kerschke, Pascal, and Heike Trautmann. “The R-Package FLACCO for Exploratory
Landscape Analysis with Applications to Multi-Objective Optimization Problems.”
Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2016,
doi:10.1109/CEC.2016.7748359.
short: 'P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary
Computation (CEC), Vancouver, BC, Kanada, 2016.'
date_created: 2023-08-04T15:14:52Z
date_updated: 2023-10-16T13:39:06Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/CEC.2016.7748359
language:
- iso: eng
place: Vancouver, BC, Kanada
publication: Proceedings of the IEEE Congress on Evolutionary Computation (CEC)
status: public
title: The R-Package FLACCO for Exploratory Landscape Analysis with Applications to
Multi-Objective Optimization Problems
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '46370'
abstract:
- lang: eng
text: This report documents the talks and discussions at the Dagstuhl Seminar 15211
"Theory of Evolutionary Algorithms". This seminar, now in its 8th edition, is
the main meeting point of the highly active theory of randomized search heuristics
subcommunities in Australia, Asia, North America, and Europe. Topics intensively
discussed include rigorous runtime analysis and computational complexity theory
for randomised search heuristics, information geometry of randomised search, and
synergies between the theory of evolutionary algorithms and theories of natural
evolution.
author:
- first_name: F
full_name: Neumann, F
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Neumann F, Trautmann H. Working Group Report: Bridging the Gap Between Experiments
and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms
(Dagstuhl Seminar 15211). Dagstuhl Reports. 2016;5(5):78–79. doi:10.4230/DagRep.5.5.57'
apa: 'Neumann, F., & Trautmann, H. (2016). Working Group Report: Bridging the
Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory
of Evolutionary Algorithms (Dagstuhl Seminar 15211). Dagstuhl Reports,
5(5), 78–79. https://doi.org/10.4230/DagRep.5.5.57'
bibtex: '@article{Neumann_Trautmann_2016, title={Working Group Report: Bridging
the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis;
Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211)}, volume={5}, DOI={10.4230/DagRep.5.5.57}, number={5},
journal={Dagstuhl Reports}, author={Neumann, F and Trautmann, Heike}, year={2016},
pages={78–79} }'
chicago: 'Neumann, F, and Heike Trautmann. “Working Group Report: Bridging the Gap
Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of
Evolutionary Algorithms (Dagstuhl Seminar 15211).” Dagstuhl Reports 5,
no. 5 (2016): 78–79. https://doi.org/10.4230/DagRep.5.5.57.'
ieee: 'F. Neumann and H. Trautmann, “Working Group Report: Bridging the Gap Between
Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary
Algorithms (Dagstuhl Seminar 15211),” Dagstuhl Reports, vol. 5, no. 5,
pp. 78–79, 2016, doi: 10.4230/DagRep.5.5.57.'
mla: 'Neumann, F., and Heike Trautmann. “Working Group Report: Bridging the Gap
Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of
Evolutionary Algorithms (Dagstuhl Seminar 15211).” Dagstuhl Reports, vol.
5, no. 5, 2016, pp. 78–79, doi:10.4230/DagRep.5.5.57.'
short: F. Neumann, H. Trautmann, Dagstuhl Reports 5 (2016) 78–79.
date_created: 2023-08-04T15:17:00Z
date_updated: 2023-10-16T13:40:00Z
department:
- _id: '34'
- _id: '819'
doi: 10.4230/DagRep.5.5.57
intvolume: ' 5'
issue: '5'
language:
- iso: eng
page: 78–79
publication: Dagstuhl Reports
status: public
title: 'Working Group Report: Bridging the Gap Between Experiments and Theory Using
Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar
15211)'
type: journal_article
user_id: '15504'
volume: 5
year: '2016'
...
---
_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: '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: '52870'
author:
- first_name: Lars Lau
full_name: Raket, Lars Lau
last_name: Raket
- first_name: Britta
full_name: Grimme, Britta
last_name: Grimme
- first_name: Gregor
full_name: Schöner, Gregor
last_name: Schöner
- first_name: Christian
full_name: Igel, Christian
last_name: Igel
- first_name: Bo
full_name: Markussen, Bo
last_name: Markussen
citation:
ama: Raket LL, Grimme B, Schöner G, Igel C, Markussen B. Separating timing, movement
conditions and individual differences in the analysis of human movement. PLoS
Computational Biology. 2016;12(9):e1005092.
apa: Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B. (2016).
Separating timing, movement conditions and individual differences in the analysis
of human movement. PLoS Computational Biology, 12(9), e1005092.
bibtex: '@article{Raket_Grimme_Schöner_Igel_Markussen_2016, title={Separating timing,
movement conditions and individual differences in the analysis of human movement},
volume={12}, number={9}, journal={PLoS Computational Biology}, publisher={Public
Library of Science San Francisco, CA USA}, author={Raket, Lars Lau and Grimme,
Britta and Schöner, Gregor and Igel, Christian and Markussen, Bo}, year={2016},
pages={e1005092} }'
chicago: 'Raket, Lars Lau, Britta Grimme, Gregor Schöner, Christian Igel, and Bo
Markussen. “Separating Timing, Movement Conditions and Individual Differences
in the Analysis of Human Movement.” PLoS Computational Biology 12, no.
9 (2016): e1005092.'
ieee: L. L. Raket, B. Grimme, G. Schöner, C. Igel, and B. Markussen, “Separating
timing, movement conditions and individual differences in the analysis of human
movement,” PLoS Computational Biology, vol. 12, no. 9, p. e1005092, 2016.
mla: Raket, Lars Lau, et al. “Separating Timing, Movement Conditions and Individual
Differences in the Analysis of Human Movement.” PLoS Computational Biology,
vol. 12, no. 9, Public Library of Science San Francisco, CA USA, 2016, p. e1005092.
short: L.L. Raket, B. Grimme, G. Schöner, C. Igel, B. Markussen, PLoS Computational
Biology 12 (2016) e1005092.
date_created: 2024-03-25T15:03:45Z
date_updated: 2026-03-19T07:48:20Z
department:
- _id: '819'
intvolume: ' 12'
issue: '9'
page: e1005092
publication: PLoS Computational Biology
publisher: Public Library of Science San Francisco, CA USA
status: public
title: Separating timing, movement conditions and individual differences in the analysis
of human movement
type: journal_article
user_id: '103682'
volume: 12
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'
...