---
_id: '48858'
abstract:
- lang: eng
  text: The $$\textbackslash mathcal NP$$-hard multi-criteria shortest path problem
    (mcSPP) is of utmost practical relevance, e.~g., in navigation system design and
    logistics. We address the problem of approximating the Pareto-front of the mcSPP
    with sum objectives. We do so by proposing a new mutation operator for multi-objective
    evolutionary algorithms that solves single-objective versions of the shortest
    path problem on subgraphs. A rigorous empirical benchmark on a diverse set of
    problem instances shows the effectiveness of the approach in comparison to a well-known
    mutation operator in terms of convergence speed and approximation quality. In
    addition, we glance at the neighbourhood structure and similarity of obtained
    Pareto-optimal solutions and derive promising directions for future work.
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. Solving Scalarized Subproblems within Evolutionary Algorithms
    for Multi-criteria Shortest Path Problems. In: Battiti R, Brunato M, Kotsireas
    I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes
    in Computer Science. Springer International Publishing; 2019:184–198. doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_17">10.1007/978-3-030-05348-2_17</a>'
  apa: Bossek, J., &#38; Grimme, C. (2019). Solving Scalarized Subproblems within
    Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti,
    M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent
    Optimization</i> (pp. 184–198). Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-05348-2_17">https://doi.org/10.1007/978-3-030-05348-2_17</a>
  bibtex: '@inproceedings{Bossek_Grimme_2019, place={Cham}, series={Lecture Notes
    in Computer Science}, title={Solving Scalarized Subproblems within Evolutionary
    Algorithms for Multi-criteria Shortest Path Problems}, DOI={<a href="https://doi.org/10.1007/978-3-030-05348-2_17">10.1007/978-3-030-05348-2_17</a>},
    booktitle={Learning and Intelligent Optimization}, publisher={Springer International
    Publishing}, author={Bossek, Jakob and Grimme, Christian}, editor={Battiti, Roberto
    and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019},
    pages={184–198}, collection={Lecture Notes in Computer Science} }'
  chicago: 'Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within
    Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In <i>Learning
    and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias
    Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science.
    Cham: Springer International Publishing, 2019. <a href="https://doi.org/10.1007/978-3-030-05348-2_17">https://doi.org/10.1007/978-3-030-05348-2_17</a>.'
  ieee: 'J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary
    Algorithms for Multi-criteria Shortest Path Problems,” in <i>Learning and Intelligent
    Optimization</i>, 2019, pp. 184–198, doi: <a href="https://doi.org/10.1007/978-3-030-05348-2_17">10.1007/978-3-030-05348-2_17</a>.'
  mla: Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within
    Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” <i>Learning
    and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International
    Publishing, 2019, pp. 184–198, doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_17">10.1007/978-3-030-05348-2_17</a>.
  short: 'J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos
    (Eds.), Learning and Intelligent Optimization, Springer International Publishing,
    Cham, 2019, pp. 184–198.'
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:44:44Z
department:
- _id: '819'
doi: 10.1007/978-3-030-05348-2_17
editor:
- first_name: Roberto
  full_name: Battiti, Roberto
  last_name: Battiti
- first_name: Mauro
  full_name: Brunato, Mauro
  last_name: Brunato
- first_name: Ilias
  full_name: Kotsireas, Ilias
  last_name: Kotsireas
- first_name: Panos M.
  full_name: Pardalos, Panos M.
  last_name: Pardalos
extern: '1'
language:
- iso: eng
page: 184–198
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
  isbn:
  - 978-3-030-05348-2
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria
  Shortest Path Problems
type: conference
user_id: '102979'
year: '2019'
...
---
_id: '48875'
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
  last_name: Trautmann
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
    PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer
    Science. Springer International Publishing; 2019:215–219. doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>'
  apa: 'Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement:
    Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I.
    Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i>
    (pp. 215–219). Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-05348-2_19">https://doi.org/10.1007/978-3-030-05348-2_19</a>'
  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}, DOI={<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>},
    booktitle={Learning and Intelligent Optimization}, publisher={Springer International
    Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto
    and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, 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 <i>Learning and Intelligent
    Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and
    Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer
    International Publishing, 2019. <a href="https://doi.org/10.1007/978-3-030-05348-2_19">https://doi.org/10.1007/978-3-030-05348-2_19</a>.'
  ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives
    to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>,
    2019, pp. 215–219, doi: <a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>.'
  mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement:
    Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent
    Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing,
    2019, pp. 215–219, doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>.'
  short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M.
    Pardalos (Eds.), Learning and Intelligent Optimization, Springer International
    Publishing, Cham, 2019, pp. 215–219.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:32Z
department:
- _id: '819'
doi: 10.1007/978-3-030-05348-2_19
editor:
- first_name: Roberto
  full_name: Battiti, Roberto
  last_name: Battiti
- first_name: Mauro
  full_name: Brunato, Mauro
  last_name: Brunato
- first_name: Ilias
  full_name: Kotsireas, Ilias
  last_name: Kotsireas
- first_name: Panos M.
  full_name: Pardalos, Panos M.
  last_name: Pardalos
extern: '1'
keyword:
- Algorithm selection
- Performance measurement
language:
- iso: eng
page: 215–219
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
  isbn:
  - 978-3-030-05348-2
publisher: Springer International Publishing
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: '102979'
year: '2019'
...