---
_id: '46339'
abstract:
- lang: eng
text: 'Evolutionary algorithms have successfully been applied to evolve problem
instances that exhibit a significant difference in performance for a given algorithm
or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP).
Creating a large variety of instances is crucial for successful applications in
the blooming field of algorithm selection. In this paper, we introduce new and
creative mutation operators for evolving instances of the TSP. We show that adopting
those operators in an evolutionary algorithm allows for the generation of benchmark
sets with highly desirable properties: (1) novelty by clear visual distinction
to established benchmark sets in the field, (2) visual and quantitative diversity
in the space of TSP problem characteristics, and (3) significant performance differences
with respect to the restart versions of heuristic state-of-the-art TSP solvers
EAX and LKH. The important aspect of diversity is addressed and achieved solely
by the proposed mutation operators and not enforced by explicit diversity preservation.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Aneta
full_name: Neumann, Aneta
last_name: Neumann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving
Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: Friedrich
T, Doerr C, Arnold D, eds. Proceedings of the 15$^th$ ACM/SIGEVO Workshop on
Foundations of Genetic Algorithms (FOGA XV). ; 2019:58–71. doi:10.1145/3299904.3340307'
apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann,
H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation
Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of
the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)
(pp. 58–71). https://doi.org/10.1145/3299904.3340307
bibtex: '@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={Potsdam,
Germany}, title={Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators}, DOI={10.1145/3299904.3340307},
booktitle={Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)}, author={Bossek, Jakob and Kerschke, Pascal and Neumann,
Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, editor={Friedrich,
Tobias and Doerr, Carola and Arnold, Dirk}, year={2019}, pages={58–71} }'
chicago: Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann,
and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative
Mutation Operators.” In Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations
of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich, Carola Doerr,
and Dirk Arnold, 58–71. Potsdam, Germany, 2019. https://doi.org/10.1145/3299904.3340307.
ieee: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann,
“Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,”
in Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV), 2019, pp. 58–71, doi: 10.1145/3299904.3340307.'
mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and
Creative Mutation Operators.” Proceedings of the 15$^th$ ACM/SIGEVO Workshop
on Foundations of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich
et al., 2019, pp. 58–71, doi:10.1145/3299904.3340307.
short: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann,
in: T. Friedrich, C. Doerr, D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO
Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 2019,
pp. 58–71.'
date_created: 2023-08-04T07:45:39Z
date_updated: 2024-06-10T11:59:26Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3299904.3340307
editor:
- first_name: Tobias
full_name: Friedrich, Tobias
last_name: Friedrich
- first_name: Carola
full_name: Doerr, Carola
last_name: Doerr
- first_name: Dirk
full_name: Arnold, Dirk
last_name: Arnold
language:
- iso: eng
page: 58–71
place: Potsdam, Germany
publication: Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA XV)
status: public
title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators
type: conference
user_id: '15504'
year: '2019'
...
---
_id: '46338'
abstract:
- lang: eng
text: We tackle a bi-objective dynamic orienteering problem where customer requests
arise as time passes by. The goal is to minimize the tour length traveled by a
single delivery vehicle while simultaneously keeping the number of dismissed dynamic
customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm
which is grounded on insights gained from a previous series of work on an a-posteriori
version of the problem, where all request times are known in advance. In our experiments,
we simulate different decision maker strategies and evaluate the development of
the Pareto-front approximations on exemplary problem instances. It turns out,
that despite severely reduced computational budget and no oracle-knowledge of
request times the dynamic EMOA is capable of producing approximations which partially
dominate the results of the a-posteriori EMOA and dynamic integer linear programming
strategies.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering:
Towards a Dynamic Multi-Objective Evolutionary Algorithm. In: Deb K, Goodman E,
Coello CCA, et al., eds. Evolutionary Multi-Criterion Optimization (EMO).
Vol 11411. Lecture Notes in Computer Science. Springer International Publishing;
2019:516–528. doi:10.1007/978-3-030-12598-1_41'
apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019).
Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.
In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim,
& P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol.
11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41'
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={East
Lansing, Michigan, USA}, series={Lecture Notes in Computer Science}, title={Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}, volume={11411},
DOI={10.1007/978-3-030-12598-1_41},
booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer
International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel,
Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and
Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen,
Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), edited
by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa
Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:516–528. Lecture Notes in
Computer Science. East Lansing, Michigan, USA: Springer International Publishing,
2019. https://doi.org/10.1007/978-3-030-12598-1_41.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective
Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm,” in Evolutionary
Multi-Criterion Optimization (EMO), 2019, vol. 11411, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.'
mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective
Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO),
edited by Kalyanmoy Deb et al., vol. 11411, Springer International Publishing,
2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.'
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E.
Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.),
Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing,
East Lansing, Michigan, USA, 2019, pp. 516–528.'
date_created: 2023-08-04T07:44:59Z
date_updated: 2024-06-10T12:00:05Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-12598-1_41
editor:
- first_name: Kalyanmoy
full_name: Deb, Kalyanmoy
last_name: Deb
- first_name: Erik
full_name: Goodman, Erik
last_name: Goodman
- first_name: Coello Carlos A.
full_name: Coello, Coello Carlos A.
last_name: Coello
- first_name: Kathrin
full_name: Klamroth, Kathrin
last_name: Klamroth
- first_name: Kaisa
full_name: Miettinen, Kaisa
last_name: Miettinen
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Patrick
full_name: Reed, Patrick
last_name: Reed
intvolume: ' 11411'
language:
- iso: eng
page: 516–528
place: East Lansing, Michigan, USA
publication: Evolutionary Multi-Criterion Optimization (EMO)
publication_identifier:
isbn:
- 978-3-030-12597-4
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary
Algorithm'
type: conference
user_id: '15504'
volume: 11411
year: '2019'
...
---
_id: '46337'
abstract:
- lang: eng
text: A multiobjective perspective onto common performance measures such as the
PAR10 score or the expected runtime of single-objective stochastic solvers is
presented by directly investigating the tradeoff between the fraction of failed
runs and the average runtime. Multi-objective indicators operating in the bi-objective
space allow for an overall performance comparison on a set of instances paving
the way for instance-based automated algorithm selection techniques.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos
P, eds. Learning and Intelligent Optimization. Vol 11353. Lecture Notes
in Computer Science. Springer; 2019:215–219.'
apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I.
Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization
(Vol. 11353, pp. 215–219). Springer.'
bibtex: '@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes
in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent
Optimization}, publisher={Springer}, author={Bossek, Jakob and Trautmann, Heike},
editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019},
pages={215–219}, collection={Lecture Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent
Optimization, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos,
11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.'
ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives
to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization,
2019, vol. 11353, pp. 215–219.'
mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent
Optimization, edited by R Battiti et al., vol. 11353, Springer, 2019, pp.
215–219.'
short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos
(Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.'
date_created: 2023-08-04T07:44:10Z
date_updated: 2024-06-10T12:00:23Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: R
full_name: Battiti, R
last_name: Battiti
- first_name: M
full_name: Brunato, M
last_name: Brunato
- first_name: I
full_name: Kotsireas, I
last_name: Kotsireas
- first_name: P
full_name: Pardalos, P
last_name: Pardalos
intvolume: ' 11353'
language:
- iso: eng
page: 215–219
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-030-05347-5
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected
Running Time'
type: conference
user_id: '15504'
volume: 11353
year: '2019'
...
---
_id: '48839'
abstract:
- lang: eng
text: We analyze the effects of including local search techniques into a multi-objective
evolutionary algorithm for solving a bi-objective orienteering problem with a
single vehicle while the two conflicting objectives are minimization of travel
time and maximization of the number of visited customer locations. Experiments
are based on a large set of specifically designed problem instances with different
characteristics and it is shown that local search techniques focusing on one of
the objectives only improve the performance of the evolutionary algorithm in terms
of both objectives. The analysis also shows that local search techniques are capable
of sending locally optimal solutions to foremost fronts of the multi-objective
optimization process, and that these solutions then become the leading factors
of the evolutionary process.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects
in Bi-Objective Orienteering. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’18. Association for Computing Machinery; 2018:585–592.
doi:10.1145/3205455.3205548'
apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018).
Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic
and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New
York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective
Orienteering}, DOI={10.1145/3205455.3205548},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, year={2018},
pages={585–592}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 585–592. GECCO ’18.
New York, NY, USA: Association for Computing Machinery, 2018. https://doi.org/10.1145/3205455.3205548.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search
Effects in Bi-Objective Orienteering,” in Proceedings of the Genetic and Evolutionary
Computation Conference, 2018, pp. 585–592, doi: 10.1145/3205455.3205548.'
mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2018, pp. 585–592, doi:10.1145/3205455.3205548.
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, New York, NY, USA, 2018, pp. 585–592.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:42:14Z
department:
- _id: '819'
doi: 10.1145/3205455.3205548
extern: '1'
keyword:
- combinatorial optimization
- metaheuristics
- multi-objective optimization
- orienteering
- transportation
language:
- iso: eng
page: 585–592
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-5618-3
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Local Search Effects in Bi-Objective Orienteering
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48867'
abstract:
- lang: eng
text: Assessing the performance of stochastic optimization algorithms in the field
of multi-objective optimization is of utmost importance. Besides the visual comparison
of the obtained approximation sets, more sophisticated methods have been proposed
in the last decade, e. g., a variety of quantitative performance indicators or
statistical tests. In this paper, we present tools implemented in the R package
ecr, which assist in performing comprehensive and sound comparison and evaluation
of multi-objective evolutionary algorithms following recommendations from the
literature.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. In: Proceedings of the Genetic and Evolutionary Computation
Conference Companion. GECCO ’18. Association for Computing Machinery; 2018:1350–1356.
doi:10.1145/3205651.3208312'
apa: Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
bibtex: '@inproceedings{Bossek_2018, place={New York, NY, USA}, series={GECCO ’18},
title={Performance Assessment of Multi-Objective Evolutionary Algorithms with
the R Package ecr}, DOI={10.1145/3205651.3208312},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob},
year={2018}, pages={1350–1356}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary
Algorithms with the R Package Ecr.” In Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 1350–1356. GECCO ’18. New York, NY, USA:
Association for Computing Machinery, 2018. https://doi.org/10.1145/3205651.3208312.'
ieee: 'J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr,” in Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 2018, pp. 1350–1356, doi: 10.1145/3205651.3208312.'
mla: Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package Ecr.” Proceedings of the Genetic and Evolutionary Computation
Conference Companion, Association for Computing Machinery, 2018, pp. 1350–1356,
doi:10.1145/3205651.3208312.
short: 'J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference
Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1350–1356.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:04Z
department:
- _id: '819'
doi: 10.1145/3205651.3208312
extern: '1'
keyword:
- evolutionary optimization
- performance assessment
- software-tools
language:
- iso: eng
page: 1350–1356
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Performance Assessment of Multi-Objective Evolutionary Algorithms with the
R Package ecr
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48885'
abstract:
- lang: eng
text: Performance comparisons of optimization algorithms are heavily influenced
by the underlying indicator(s). In this paper we investigate commonly used performance
indicators for single-objective stochastic solvers, such as the Penalized Average
Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark
performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a
methodology for analyzing the effects of (usually heuristically set) indicator
parametrizations - such as the penalty factor and the method used for aggregating
across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings
of the Genetic and Evolutionary Computation Conference Companion. GECCO’18.
Association for Computing Machinery; 2018:1737–1744. doi:10.1145/3205651.3208233'
apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of
State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP
Solvers. Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA},
series={GECCO’18}, title={Parameterization of State-of-the-Art Performance Indicators:
A Robustness Study Based on Inexact TSP Solvers}, DOI={10.1145/3205651.3208233},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Kerschke,
Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744},
collection={GECCO’18} }'
chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing
Machinery, 2018. https://doi.org/10.1145/3205651.3208233.'
ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art
Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 2018, pp.
1737–1744, doi: 10.1145/3205651.3208233.'
mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of
the Genetic and Evolutionary Computation Conference Companion, Association
for Computing Machinery, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.'
short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and
Evolutionary Computation Conference Companion, Association for Computing Machinery,
New York, NY, USA, 2018, pp. 1737–1744.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:38Z
department:
- _id: '819'
doi: 10.1145/3205651.3208233
extern: '1'
keyword:
- algorithm selection
- optimization
- performance measures
- transportation
- travelling salesperson problem
language:
- iso: eng
page: 1737–1744
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publisher: Association for Computing Machinery
series_title: GECCO’18
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48880'
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: Grimme C, Bossek J. Einführung in Die Optimierung - Konzepte, Methoden Und
Anwendungen. Springer Vieweg; 2018. doi:10.1007/978-3-658-21151-6
apa: Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen. Springer Vieweg. https://doi.org/10.1007/978-3-658-21151-6
bibtex: '@book{Grimme_Bossek_2018, title={Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen}, DOI={10.1007/978-3-658-21151-6},
publisher={Springer Vieweg}, author={Grimme, Christian and Bossek, Jakob}, year={2018}
}'
chicago: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung -
Konzepte, Methoden Und Anwendungen. Springer Vieweg, 2018. https://doi.org/10.1007/978-3-658-21151-6.
ieee: C. Grimme and J. Bossek, Einführung in die Optimierung - Konzepte, Methoden
und Anwendungen. Springer Vieweg, 2018.
mla: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung - Konzepte,
Methoden Und Anwendungen. Springer Vieweg, 2018, doi:10.1007/978-3-658-21151-6.
short: C. Grimme, J. Bossek, Einführung in Die Optimierung - Konzepte, Methoden
Und Anwendungen, Springer Vieweg, 2018.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:57Z
department:
- _id: '819'
doi: 10.1007/978-3-658-21151-6
extern: '1'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-658-21150-9
publisher: Springer Vieweg
status: public
title: Einführung in die Optimierung - Konzepte, Methoden und Anwendungen
type: book
user_id: '102979'
year: '2018'
...
---
_id: '48884'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
on a large set of well-known benchmark instances and demonstrate complementary
performance, in that different instances may be solved most effectively by different
algorithms. We leverage this complementarity to build an algorithm selector, which
selects the best TSP solver on a per-instance basis and thus achieves significantly
improved performance compared to the single best solver, representing an advance
in the state of the art in solving the Euclidean TSP. Our in-depth analysis of
the selectors provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620.
doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff,
Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018},
pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation,
vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...
---
_id: '48866'
abstract:
- lang: eng
text: 'Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Grapherator: A Modular Multi-Step Graph Generator. Journal of
Open Source Software. 2018;3(22):528. doi:10.21105/joss.00528'
apa: 'Bossek, J. (2018). Grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528. https://doi.org/10.21105/joss.00528'
bibtex: '@article{Bossek_2018, title={Grapherator: A Modular Multi-Step Graph Generator},
volume={3}, DOI={10.21105/joss.00528},
number={22}, journal={Journal of Open Source Software}, author={Bossek, Jakob},
year={2018}, pages={528} }'
chicago: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software 3, no. 22 (2018): 528. https://doi.org/10.21105/joss.00528.'
ieee: 'J. Bossek, “Grapherator: A Modular Multi-Step Graph Generator,” Journal
of Open Source Software, vol. 3, no. 22, p. 528, 2018, doi: 10.21105/joss.00528.'
mla: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software, vol. 3, no. 22, 2018, p. 528, doi:10.21105/joss.00528.'
short: J. Bossek, Journal of Open Source Software 3 (2018) 528.
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:51:50Z
department:
- _id: '819'
doi: 10.21105/joss.00528
intvolume: ' 3'
issue: '22'
language:
- iso: eng
page: '528'
publication: Journal of Open Source Software
publication_identifier:
issn:
- 2475-9066
status: public
title: 'Grapherator: A Modular Multi-Step Graph Generator'
type: journal_article
user_id: '102979'
volume: 3
year: '2018'
...
---
_id: '46348'
abstract:
- lang: eng
text: We analyze the effects of including local search techniques into a multi-objective
evolutionary algorithm for solving a bi-objective orienteering problem with a
single vehicle while the two conflicting objectives are minimization of travel
time and maximization of the number of visited customer locations. Experiments
are based on a large set of specifically designed problem instances with different
characteristics and it is shown that local search techniques focusing on one of
the objectives only improve the performance of the evolutionary algorithm in terms
of both objectives. The analysis also shows that local search techniques are capable
of sending locally optimal solutions to foremost fronts of the multi-objective
optimization process, and that these solutions then become the leading factors
of the evolutionary process.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Guenter
full_name: Rudolph, Guenter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects
in Bi-Objective Orienteering. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’18. ACM; 2018:585–592. doi:10.1145/3205455.3205548'
apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018).
Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic
and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New
York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective
Orienteering}, DOI={10.1145/3205455.3205548},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan
and Rudolph, Guenter and Trautmann, Heike}, year={2018}, pages={585–592}, collection={GECCO
’18} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Guenter Rudolph, and
Heike Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 585–592. GECCO ’18.
New York, NY, USA: ACM, 2018. https://doi.org/10.1145/3205455.3205548.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search
Effects in Bi-Objective Orienteering,” in Proceedings of the Genetic and Evolutionary
Computation Conference, 2018, pp. 585–592, doi: 10.1145/3205455.3205548.'
mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”
Proceedings of the Genetic and Evolutionary Computation Conference, ACM,
2018, pp. 585–592, doi:10.1145/3205455.3205548.
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings
of the Genetic and Evolutionary Computation Conference, ACM, New York, NY, USA,
2018, pp. 585–592.'
date_created: 2023-08-04T07:53:16Z
date_updated: 2024-06-10T11:59:09Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3205455.3205548
language:
- iso: eng
page: 585–592
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-5618-3
publisher: ACM
series_title: GECCO ’18
status: public
title: Local Search Effects in Bi-Objective Orienteering
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '46352'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large
set of well-known benchmark instances and demonstrate complementary performance,
in that different instances may be solved most effectively by different algorithms.
We leverage this complementarity to build an algorithm selector, which selects
the best TSP solver on a per-instance basis and thus achieves significantly improved
performance compared to the single best solver, representing an advance in the
state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors
provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation (ECJ).
2018;26(4):597–620. doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation (ECJ), 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal
and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike},
year={2018}, pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation (ECJ) 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation
(ECJ), vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation (ECJ), vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation (ECJ) 26 (2018) 597–620.
date_created: 2023-08-04T07:56:15Z
date_updated: 2024-06-10T11:58:38Z
department:
- _id: '34'
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation (ECJ)
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '15504'
volume: 26
year: '2018'
...
---
_id: '46349'
abstract:
- lang: eng
text: Performance comparisons of optimization algorithms are heavily influenced
by the underlying indicator(s). In this paper we investigate commonly used performance
indicators for single-objective stochastic solvers, such as the Penalized Average
Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark
performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a
methodology for analyzing the effects of (usually heuristically set) indicator
parametrizations - such as the penalty factor and the method used for aggregating
across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion.
; 2018:1737–1744. doi:10.1145/3205651.3208233'
apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of
State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP
Solvers. Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={Kyoto, Japan}, title={Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers}, DOI={10.1145/3205651.3208233},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann,
Heike}, year={2018}, pages={1737–1744} }'
chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO ’18) Companion, 1737–1744. Kyoto, Japan, 2018. https://doi.org/10.1145/3205651.3208233.'
ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art
Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion,
2018, pp. 1737–1744, doi: 10.1145/3205651.3208233.'
mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion,
2018, pp. 1737–1744, doi:10.1145/3205651.3208233.'
short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO ’18) Companion, Kyoto, Japan, 2018,
pp. 1737–1744.'
date_created: 2023-08-04T07:53:59Z
date_updated: 2024-06-10T11:58:54Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3205651.3208233
language:
- iso: eng
page: 1737–1744
place: Kyoto, Japan
publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
’18) Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7/18/07
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '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: '48873'
abstract:
- lang: eng
text: Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP)
heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful
in generating satisfactory or even optimal solutions. However, the reasons for
their success are not yet fully understood. Recent approaches take an analytical
viewpoint and try to identify instance features, which make an instance hard or
easy to solve. We contribute to this area by generating instance sets for couples
of TSP algorithms A and B by maximizing/minimizing their performance difference
in order to generate instances which are easier to solve for one solver and much
harder to solve for the other. This instance set offers the potential to identify
key features which allow to distinguish between the problem hardness classes of
both algorithms.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J,
eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science.
Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4'
apa: Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann,
& J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp.
48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes
in Computer Science}, title={Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers}, DOI={10.1007/978-3-319-50349-3_4},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, Paola
and Sellmann, Meinolf and Vanschoren, Joaquin}, year={2016}, pages={48–59}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing
Performance Differences of State-of-the-Art Inexact TSP Solvers.” In Learning
and Intelligent Optimization, edited by Paola Festa, Meinolf Sellmann, and
Joaquin Vanschoren, 48–59. Lecture Notes in Computer Science. Cham: Springer International
Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_4.'
ieee: 'J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent
Optimization, 2016, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.'
mla: Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers.” Learning and Intelligent
Optimization, edited by Paola Festa et al., Springer International Publishing,
2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
short: 'J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.),
Learning and Intelligent Optimization, Springer International Publishing, Cham,
2016, pp. 48–59.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:05Z
department:
- _id: '819'
doi: 10.1007/978-3-319-50349-3_4
editor:
- first_name: Paola
full_name: Festa, Paola
last_name: Festa
- first_name: Meinolf
full_name: Sellmann, Meinolf
last_name: Sellmann
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
extern: '1'
keyword:
- Algorithm selection
- Feature selection
- Instance hardness
- TSP
language:
- iso: eng
page: 48–59
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-319-50349-3
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Evolving Instances for Maximizing Performance Differences of State-of-the-Art
Inexact TSP Solvers
type: conference
user_id: '102979'
year: '2016'
...
---
_id: '48874'
abstract:
- lang: eng
text: State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem
TSP are known to mostly yield high-quality solutions in reasonable computation
times. With the purpose of understanding different levels of instance difficulties,
instances for the current State of the Art heuristic TSP solvers LKH+restart and
EAX+restart are presented which are evolved using a sophisticated evolutionary
algorithm. More specifically, the performance differences of the respective solvers
are maximized resulting in instances which are easier to solve for one solver
and much more difficult for the other. Focusing on both optimization directions,
instance features are identified which characterize both types of instances and
increase the understanding of solver performance differences.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
In: Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037.
AI*IA 2016. Springer-Verlag; 2016:3–12. doi:10.1007/978-3-319-49130-1_1'
apa: Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
Proceedings of the XV International Conference of the Italian Association for
Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
3–12. https://doi.org/10.1007/978-3-319-49130-1_1
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Berlin, Heidelberg}, series={AI*IA
2016}, title={Understanding Characteristics of Evolved Instances for State-of-the-Art
Inexact TSP Solvers with Maximum Performance Difference}, DOI={10.1007/978-3-319-49130-1_1},
booktitle={Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037},
publisher={Springer-Verlag}, author={Bossek, Jakob and Trautmann, Heike}, year={2016},
pages={3–12}, collection={AI*IA 2016} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of
Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance
Difference.” In Proceedings of the XV International Conference of the Italian
Association for Artificial Intelligence on Advances in Artificial Intelligence
- Volume 10037, 3–12. AI*IA 2016. Berlin, Heidelberg: Springer-Verlag, 2016.
https://doi.org/10.1007/978-3-319-49130-1_1.'
ieee: 'J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances
for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,”
in Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
2016, pp. 3–12, doi: 10.1007/978-3-319-49130-1_1.'
mla: Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved
Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.”
Proceedings of the XV International Conference of the Italian Association for
Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037,
Springer-Verlag, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1.
short: 'J. Bossek, H. Trautmann, in: Proceedings of the XV International Conference
of the Italian Association for Artificial Intelligence on Advances in Artificial
Intelligence - Volume 10037, Springer-Verlag, Berlin, Heidelberg, 2016, pp. 3–12.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:11Z
doi: 10.1007/978-3-319-49130-1_1
extern: '1'
keyword:
- Combinatorial optimization
- Instance hardness
- Metaheuristics
- Transportation
- TSP
language:
- iso: eng
page: 3–12
place: Berlin, Heidelberg
publication: Proceedings of the XV International Conference of the Italian Association
for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037
publication_identifier:
isbn:
- 978-3-319-49129-5
publication_status: published
publisher: Springer-Verlag
series_title: AI*IA 2016
status: public
title: Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact
TSP Solvers with Maximum Performance Difference
type: conference
user_id: '102979'
year: '2016'
...