---
_id: '48867'
abstract:
- lang: eng
text: Assessing the performance of stochastic optimization algorithms in the field
of multi-objective optimization is of utmost importance. Besides the visual comparison
of the obtained approximation sets, more sophisticated methods have been proposed
in the last decade, e. g., a variety of quantitative performance indicators or
statistical tests. In this paper, we present tools implemented in the R package
ecr, which assist in performing comprehensive and sound comparison and evaluation
of multi-objective evolutionary algorithms following recommendations from the
literature.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. In: Proceedings of the Genetic and Evolutionary Computation
Conference Companion. GECCO ’18. Association for Computing Machinery; 2018:1350–1356.
doi:10.1145/3205651.3208312'
apa: Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
bibtex: '@inproceedings{Bossek_2018, place={New York, NY, USA}, series={GECCO ’18},
title={Performance Assessment of Multi-Objective Evolutionary Algorithms with
the R Package ecr}, DOI={10.1145/3205651.3208312},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob},
year={2018}, pages={1350–1356}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary
Algorithms with the R Package Ecr.” In Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 1350–1356. GECCO ’18. New York, NY, USA:
Association for Computing Machinery, 2018. https://doi.org/10.1145/3205651.3208312.'
ieee: 'J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr,” in Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 2018, pp. 1350–1356, doi: 10.1145/3205651.3208312.'
mla: Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package Ecr.” Proceedings of the Genetic and Evolutionary Computation
Conference Companion, Association for Computing Machinery, 2018, pp. 1350–1356,
doi:10.1145/3205651.3208312.
short: 'J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference
Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1350–1356.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:04Z
department:
- _id: '819'
doi: 10.1145/3205651.3208312
extern: '1'
keyword:
- evolutionary optimization
- performance assessment
- software-tools
language:
- iso: eng
page: 1350–1356
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Performance Assessment of Multi-Objective Evolutionary Algorithms with the
R Package ecr
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48885'
abstract:
- lang: eng
text: Performance comparisons of optimization algorithms are heavily influenced
by the underlying indicator(s). In this paper we investigate commonly used performance
indicators for single-objective stochastic solvers, such as the Penalized Average
Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark
performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a
methodology for analyzing the effects of (usually heuristically set) indicator
parametrizations - such as the penalty factor and the method used for aggregating
across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings
of the Genetic and Evolutionary Computation Conference Companion. GECCO’18.
Association for Computing Machinery; 2018:1737–1744. doi:10.1145/3205651.3208233'
apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of
State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP
Solvers. Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA},
series={GECCO’18}, title={Parameterization of State-of-the-Art Performance Indicators:
A Robustness Study Based on Inexact TSP Solvers}, DOI={10.1145/3205651.3208233},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Kerschke,
Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744},
collection={GECCO’18} }'
chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing
Machinery, 2018. https://doi.org/10.1145/3205651.3208233.'
ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art
Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 2018, pp.
1737–1744, doi: 10.1145/3205651.3208233.'
mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of
the Genetic and Evolutionary Computation Conference Companion, Association
for Computing Machinery, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.'
short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and
Evolutionary Computation Conference Companion, Association for Computing Machinery,
New York, NY, USA, 2018, pp. 1737–1744.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:38Z
department:
- _id: '819'
doi: 10.1145/3205651.3208233
extern: '1'
keyword:
- algorithm selection
- optimization
- performance measures
- transportation
- travelling salesperson problem
language:
- iso: eng
page: 1737–1744
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publisher: Association for Computing Machinery
series_title: GECCO’18
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '102979'
year: '2018'
...