---
_id: '48848'
abstract:
- lang: eng
  text: We build upon a recently proposed multi-objective view onto performance measurement
    of single-objective stochastic solvers. The trade-off between the fraction of
    failed runs and the mean runtime of successful runs \textendash both to be minimized
    \textendash is directly analyzed based on a study on algorithm selection of inexact
    state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover,
    we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization
    for simultaneously assessing both conflicting objectives and investigate relations
    to commonly used performance indicators, both theoretically and empirically. Next
    to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV
    measure is used as a core concept within the construction of per-instance algorithm
    selection models offering interesting insights into complementary behavior of
    inexact TSP solvers. \textbullet The multi-objective perspective is naturally
    generalizable to multiple objectives. \textbullet Proof of relationship between
    HV and the PAR in the considered bi-objective space. \textbullet New insights
    into complementary behavior of stochastic optimization algorithms.
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: Heike
  full_name: Trautmann, Heike
  last_name: Trautmann
citation:
  ama: Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance
    Assessment and Automated Selection of Single-Objective Optimization Algorithms.
    <i>Applied Soft Computing</i>. 2020;88(C). doi:<a href="https://doi.org/10.1016/j.asoc.2019.105901">10.1016/j.asoc.2019.105901</a>
  apa: Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A Multi-Objective Perspective
    on Performance Assessment and Automated Selection of Single-Objective Optimization
    Algorithms. <i>Applied Soft Computing</i>, <i>88</i>(C). <a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>
  bibtex: '@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective
    on Performance Assessment and Automated Selection of Single-Objective Optimization
    Algorithms}, volume={88}, DOI={<a href="https://doi.org/10.1016/j.asoc.2019.105901">10.1016/j.asoc.2019.105901</a>},
    number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke,
    Pascal and Trautmann, Heike}, year={2020} }'
  chicago: Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective
    Perspective on Performance Assessment and Automated Selection of Single-Objective
    Optimization Algorithms.” <i>Applied Soft Computing</i> 88, no. C (2020). <a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>.
  ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective
    on Performance Assessment and Automated Selection of Single-Objective Optimization
    Algorithms,” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi: <a href="https://doi.org/10.1016/j.asoc.2019.105901">10.1016/j.asoc.2019.105901</a>.'
  mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment
    and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied
    Soft Computing</i>, vol. 88, no. C, 2020, doi:<a href="https://doi.org/10.1016/j.asoc.2019.105901">10.1016/j.asoc.2019.105901</a>.
  short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:52:17Z
department:
- _id: '819'
doi: 10.1016/j.asoc.2019.105901
intvolume: '        88'
issue: C
keyword:
- Algorithm selection
- Combinatorial optimization
- Multi-objective optimization
- Performance measurement
- Traveling Salesperson Problem
language:
- iso: eng
publication: Applied Soft Computing
publication_identifier:
  issn:
  - 1568-4946
status: public
title: A Multi-Objective Perspective on Performance Assessment and Automated Selection
  of Single-Objective Optimization Algorithms
type: journal_article
user_id: '102979'
volume: 88
year: '2020'
...
---
_id: '46334'
abstract:
- lang: eng
  text: We build upon a recently proposed multi-objective view onto performance measurement
    of single-objective stochastic solvers. The trade-off between the fraction of
    failed runs and the mean runtime of successful runs – both to be minimized – is
    directly analyzed based on a study on algorithm selection of inexact state-of-the-art
    solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt
    the hypervolume indicator (HV) commonly used in multi-objective optimization for
    simultaneously assessing both conflicting objectives and investigate relations
    to commonly used performance indicators, both theoretically and empirically. Next
    to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV
    measure is used as a core concept within the construction of per-instance algorithm
    selection models offering interesting insights into complementary behavior of
    inexact TSP solvers.
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: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
citation:
  ama: Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance
    assessment and automated selection of single-objective optimization algorithms.
    <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>
  apa: Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective
    on performance assessment and automated selection of single-objective optimization
    algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>
  bibtex: '@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective
    on performance assessment and automated selection of single-objective optimization
    algorithms}, volume={88}, DOI={<a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>},
    journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and
    Trautmann, Heike}, year={2020}, pages={105901} }'
  chicago: 'Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective
    Perspective on Performance Assessment and Automated Selection of Single-Objective
    Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a
    href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>.'
  ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective
    on performance assessment and automated selection of single-objective optimization
    algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a
    href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>.'
  mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment
    and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied
    Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href="https://doi.org/10.1016/j.asoc.2019.105901">https://doi.org/10.1016/j.asoc.2019.105901</a>.
  short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.
date_created: 2023-08-04T07:42:26Z
date_updated: 2024-06-10T12:00:46Z
department:
- _id: '34'
- _id: '819'
doi: https://doi.org/10.1016/j.asoc.2019.105901
intvolume: '        88'
keyword:
- Algorithm selection
- Multi-objective optimization
- Performance measurement
- Combinatorial optimization
- Traveling Salesperson Problem
language:
- iso: eng
page: '105901'
publication: Applied Soft Computing
publication_identifier:
  issn:
  - 1568-4946
status: public
title: A multi-objective perspective on performance assessment and automated selection
  of single-objective optimization algorithms
type: journal_article
user_id: '15504'
volume: 88
year: '2020'
...
---
_id: '48841'
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
  last_name: Trautmann
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 Coello CA, et al., eds. <i>Evolutionary Multi-Criterion Optimization (EMO)</i>.
    Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528.
    doi:<a href="https://doi.org/10.1007/978-3-030-12598-1_41">10.1007/978-3-030-12598-1_41</a>'
  apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2019).
    Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm.
    In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim,
    &#38; P. Reed (Eds.), <i>Evolutionary Multi-Criterion Optimization (EMO)</i> (pp.
    516–528). Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-12598-1_41">https://doi.org/10.1007/978-3-030-12598-1_41</a>'
  bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={Cham},
    series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering:
    Towards a Dynamic Multi-objective Evolutionary Algorithm}, DOI={<a href="https://doi.org/10.1007/978-3-030-12598-1_41">10.1007/978-3-030-12598-1_41</a>},
    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 <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited
    by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa
    Miettinen, Sanaz Mostaghim, and Patrick Reed, 516–528. Lecture Notes in Computer
    Science. Cham: Springer International Publishing, 2019. <a href="https://doi.org/10.1007/978-3-030-12598-1_41">https://doi.org/10.1007/978-3-030-12598-1_41</a>.'
  ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective
    Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in <i>Evolutionary
    Multi-Criterion Optimization (EMO)</i>, 2019, pp. 516–528, doi: <a href="https://doi.org/10.1007/978-3-030-12598-1_41">10.1007/978-3-030-12598-1_41</a>.'
  mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective
    Evolutionary Algorithm.” <i>Evolutionary Multi-Criterion Optimization (EMO)</i>,
    edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528,
    doi:<a href="https://doi.org/10.1007/978-3-030-12598-1_41">10.1007/978-3-030-12598-1_41</a>.'
  short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E.
    Goodman, C.A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed
    (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International
    Publishing, Cham, 2019, pp. 516–528.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:43:07Z
department:
- _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: Carlos A.
  full_name: Coello Coello, Carlos A.
  last_name: Coello 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
extern: '1'
keyword:
- Combinatorial optimization
- Dynamic optimization
- Metaheuristics
- Multi-objective optimization
- Vehicle routing
language:
- iso: eng
page: 516–528
place: Cham
publication: Evolutionary Multi-Criterion Optimization (EMO)
publication_identifier:
  isbn:
  - 978-3-030-12598-1
publication_status: published
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: '102979'
year: '2019'
...
---
_id: '48840'
abstract:
- lang: eng
  text: Research has shown that for many single-objective graph problems where optimum
    solutions are composed of low weight sub-graphs, such as the minimum spanning
    tree problem (MST), mutation operators favoring low weight edges show superior
    performance. Intuitively, similar observations should hold for multi-criteria
    variants of such problems. In this work, we focus on the multi-criteria MST problem.
    A thorough experimental study is conducted where we estimate the probability of
    edges being part of non-dominated spanning trees as a function of the edges’ non-domination
    level or domination count, respectively. Building on gained insights, we propose
    several biased one-edge-exchange mutation operators that differ in the used edge-selection
    probability distribution (biased towards edges of low rank). Our empirical analysis
    shows that among different graph types (dense and sparse) and edge weight types
    (both uniformly random and combinations of Euclidean and uniformly random) biased
    edge-selection strategies perform superior in contrast to the baseline uniform
    edge-selection. Our findings are in particular strong for dense graphs.
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: Frank
  full_name: Neumann, Frank
  last_name: Neumann
citation:
  ama: 'Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation
    for the Multi-Criteria Spanning Tree Problem. In: <i>Proceedings of the Genetic
    and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing
    Machinery; 2019:516–523. doi:<a href="https://doi.org/10.1145/3321707.3321818">10.1145/3321707.3321818</a>'
  apa: Bossek, J., Grimme, C., &#38; Neumann, F. (2019). On the Benefits of Biased
    Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. <i>Proceedings
    of the Genetic and Evolutionary Computation Conference</i>, 516–523. <a href="https://doi.org/10.1145/3321707.3321818">https://doi.org/10.1145/3321707.3321818</a>
  bibtex: '@inproceedings{Bossek_Grimme_Neumann_2019, place={New York, NY, USA}, series={GECCO
    ’19}, title={On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria
    Spanning Tree Problem}, DOI={<a href="https://doi.org/10.1145/3321707.3321818">10.1145/3321707.3321818</a>},
    booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
    publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
    Christian and Neumann, Frank}, year={2019}, pages={516–523}, collection={GECCO
    ’19} }'
  chicago: 'Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of
    Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In
    <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523.
    GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href="https://doi.org/10.1145/3321707.3321818">https://doi.org/10.1145/3321707.3321818</a>.'
  ieee: 'J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange
    Mutation for the Multi-Criteria Spanning Tree Problem,” in <i>Proceedings of the
    Genetic and Evolutionary Computation Conference</i>, 2019, pp. 516–523, doi: <a
    href="https://doi.org/10.1145/3321707.3321818">10.1145/3321707.3321818</a>.'
  mla: Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for
    the Multi-Criteria Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary
    Computation Conference</i>, Association for Computing Machinery, 2019, pp. 516–523,
    doi:<a href="https://doi.org/10.1145/3321707.3321818">10.1145/3321707.3321818</a>.
  short: 'J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary
    Computation Conference, Association for Computing Machinery, New York, NY, USA,
    2019, pp. 516–523.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:42:24Z
department:
- _id: '819'
doi: 10.1145/3321707.3321818
extern: '1'
keyword:
- biased mutation
- combinatorial optimization
- minimum spanning tree
- multi-objective optimization
language:
- iso: eng
page: 516–523
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
  isbn:
  - 978-1-4503-6111-8
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’19
status: public
title: On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning
  Tree Problem
type: conference
user_id: '102979'
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: <i>Proceedings of the Genetic and Evolutionary
    Computation Conference</i>. GECCO ’18. Association for Computing Machinery; 2018:585–592.
    doi:<a href="https://doi.org/10.1145/3205455.3205548">10.1145/3205455.3205548</a>'
  apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2018).
    Local Search Effects in Bi-Objective Orienteering. <i>Proceedings of the Genetic
    and Evolutionary Computation Conference</i>, 585–592. <a href="https://doi.org/10.1145/3205455.3205548">https://doi.org/10.1145/3205455.3205548</a>
  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={<a href="https://doi.org/10.1145/3205455.3205548">10.1145/3205455.3205548</a>},
    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 <i>Proceedings
    of the Genetic and Evolutionary Computation Conference</i>, 585–592. GECCO ’18.
    New York, NY, USA: Association for Computing Machinery, 2018. <a href="https://doi.org/10.1145/3205455.3205548">https://doi.org/10.1145/3205455.3205548</a>.'
  ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search
    Effects in Bi-Objective Orienteering,” in <i>Proceedings of the Genetic and Evolutionary
    Computation Conference</i>, 2018, pp. 585–592, doi: <a href="https://doi.org/10.1145/3205455.3205548">10.1145/3205455.3205548</a>.'
  mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”
    <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association
    for Computing Machinery, 2018, pp. 585–592, doi:<a href="https://doi.org/10.1145/3205455.3205548">10.1145/3205455.3205548</a>.
  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: '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: <i>Proceedings of the XV International Conference of the Italian Association
    for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>.
    AI*IA 2016. Springer-Verlag; 2016:3–12. doi:<a href="https://doi.org/10.1007/978-3-319-49130-1_1">10.1007/978-3-319-49130-1_1</a>'
  apa: Bossek, J., &#38; Trautmann, H. (2016). Understanding Characteristics of Evolved
    Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.
    <i>Proceedings of the XV International Conference of the Italian Association for
    Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>,
    3–12. <a href="https://doi.org/10.1007/978-3-319-49130-1_1">https://doi.org/10.1007/978-3-319-49130-1_1</a>
  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={<a href="https://doi.org/10.1007/978-3-319-49130-1_1">10.1007/978-3-319-49130-1_1</a>},
    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 <i>Proceedings of the XV International Conference of the Italian
    Association for Artificial Intelligence on Advances in Artificial Intelligence
    - Volume 10037</i>, 3–12. AI*IA 2016. Berlin, Heidelberg: Springer-Verlag, 2016.
    <a href="https://doi.org/10.1007/978-3-319-49130-1_1">https://doi.org/10.1007/978-3-319-49130-1_1</a>.'
  ieee: 'J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances
    for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,”
    in <i>Proceedings of the XV International Conference of the Italian Association
    for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>,
    2016, pp. 3–12, doi: <a href="https://doi.org/10.1007/978-3-319-49130-1_1">10.1007/978-3-319-49130-1_1</a>.'
  mla: Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved
    Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.”
    <i>Proceedings of the XV International Conference of the Italian Association for
    Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>,
    Springer-Verlag, 2016, pp. 3–12, doi:<a href="https://doi.org/10.1007/978-3-319-49130-1_1">10.1007/978-3-319-49130-1_1</a>.
  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'
...
---
_id: '48887'
abstract:
- lang: eng
  text: 'We evaluate the performance of a multi-objective evolutionary algorithm on
    a class of dynamic routing problems with a single vehicle. In particular we focus
    on relating algorithmic performance to the most prominent characteristics of problem
    instances. The routing problem considers two types of customers: mandatory customers
    must be visited whereas optional customers do not necessarily have to be visited.
    Moreover, mandatory customers are known prior to the start of the tour whereas
    optional customers request for service at later points in time with the vehicle
    already being on its way. The multi-objective optimization problem then results
    as maximizing the number of visited customers while simultaneously minimizing
    total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm
    aims at approximating the related Pareto set for specifically designed benchmarking
    instances differing in terms of number of customers, geographical layout, fraction
    of mandatory customers, and request times of optional customers. Conceptional
    and experimental comparisons to online heuristic procedures are provided.'
author:
- first_name: Stephan
  full_name: Meisel, Stephan
  last_name: Meisel
- first_name: Christian
  full_name: Grimme, Christian
  last_name: Grimme
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Martin
  full_name: Wölck, Martin
  last_name: Wölck
- first_name: Günter
  full_name: Rudolph, Günter
  last_name: Rudolph
- first_name: Heike
  full_name: Trautmann, Heike
  last_name: Trautmann
citation:
  ama: 'Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation
    of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.
    In: <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>.
    GECCO’15. Association for Computing Machinery; 2015:425–432. doi:<a href="https://doi.org/10.1145/2739480.2754705">10.1145/2739480.2754705</a>'
  apa: Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., &#38; Trautmann,
    H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic
    Routing of a Vehicle. <i>Proceedings of the Genetic and Evolutionary Computation
    Conference </i>, 425–432. <a href="https://doi.org/10.1145/2739480.2754705">https://doi.org/10.1145/2739480.2754705</a>
  bibtex: '@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={New
    York, NY, USA}, series={GECCO’15}, title={Evaluation of a Multi-Objective EA on
    Benchmark Instances for Dynamic Routing of a Vehicle}, DOI={<a href="https://doi.org/10.1145/2739480.2754705">10.1145/2739480.2754705</a>},
    booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
    }, publisher={Association for Computing Machinery}, author={Meisel, Stephan and
    Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and
    Trautmann, Heike}, year={2015}, pages={425–432}, collection={GECCO’15} }'
  chicago: 'Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Günter
    Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark
    Instances for Dynamic Routing of a Vehicle.” In <i>Proceedings of the Genetic
    and Evolutionary Computation Conference </i>, 425–432. GECCO’15. New York, NY,
    USA: Association for Computing Machinery, 2015. <a href="https://doi.org/10.1145/2739480.2754705">https://doi.org/10.1145/2739480.2754705</a>.'
  ieee: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann,
    “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
    of a Vehicle,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference
    </i>, 2015, pp. 425–432, doi: <a href="https://doi.org/10.1145/2739480.2754705">10.1145/2739480.2754705</a>.'
  mla: Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances
    for Dynamic Routing of a Vehicle.” <i>Proceedings of the Genetic and Evolutionary
    Computation Conference </i>, Association for Computing Machinery, 2015, pp. 425–432,
    doi:<a href="https://doi.org/10.1145/2739480.2754705">10.1145/2739480.2754705</a>.
  short: 'S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in:
    Proceedings of the Genetic and Evolutionary Computation Conference , Association
    for Computing Machinery, New York, NY, USA, 2015, pp. 425–432.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:49:06Z
department:
- _id: '819'
doi: 10.1145/2739480.2754705
extern: '1'
keyword:
- combinatorial optimization
- metaheuristics
- multi-objective optimization
- online algorithms
- transportation
language:
- iso: eng
page: 425–432
place: New York, NY, USA
publication: 'Proceedings of the Genetic and Evolutionary Computation Conference '
publication_identifier:
  isbn:
  - 978-1-4503-3472-3
publisher: Association for Computing Machinery
series_title: GECCO’15
status: public
title: Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing
  of a Vehicle
type: conference
user_id: '102979'
year: '2015'
...