---
_id: '48894'
abstract:
- lang: eng
  text: Recently different evolutionary computation approaches have been developed
    that generate sets of high quality diverse solutions for a given optimisation
    problem. Many studies have considered diversity 1) as a mean to explore niches
    in behavioural space (quality diversity) or 2) to increase the structural differences
    of solutions (evolutionary diversity optimisation). In this study, we introduce
    a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component
    traveling thief problem. The results show the capability of the co-evolutionary
    algorithm to achieve significantly higher diversity compared to the baseline evolutionary
    diversity algorithms from the literature.
author:
- first_name: Adel
  full_name: Nikfarjam, Adel
  last_name: Nikfarjam
- first_name: Aneta
  full_name: Neumann, Aneta
  last_name: Neumann
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Frank
  full_name: Neumann, Frank
  last_name: Neumann
citation:
  ama: 'Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation
    for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke
    P, Ochoa G, Tu\v sar T, eds. <i>Parallel Problem Solving from Nature (PPSN XVII)</i>.
    Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249.
    doi:<a href="https://doi.org/10.1007/978-3-031-14714-2_17">10.1007/978-3-031-14714-2_17</a>'
  apa: Nikfarjam, A., Neumann, A., Bossek, J., &#38; Neumann, F. (2022). Co-Evolutionary
    Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova,
    H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tu\v sar (Eds.), <i>Parallel Problem
    Solving from Nature (PPSN XVII)</i> (pp. 237–249). Springer International Publishing.
    <a href="https://doi.org/10.1007/978-3-031-14714-2_17">https://doi.org/10.1007/978-3-031-14714-2_17</a>
  bibtex: '@inproceedings{Nikfarjam_Neumann_Bossek_Neumann_2022, place={Cham}, series={Lecture
    Notes in Computer Science}, title={Co-Evolutionary Diversity Optimisation for
    the Traveling Thief Problem}, DOI={<a href="https://doi.org/10.1007/978-3-031-14714-2_17">10.1007/978-3-031-14714-2_17</a>},
    booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer
    International Publishing}, author={Nikfarjam, Adel and Neumann, Aneta and Bossek,
    Jakob and Neumann, Frank}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre,
    Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}, year={2022},
    pages={237–249}, collection={Lecture Notes in Computer Science} }'
  chicago: 'Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary
    Diversity Optimisation for the Traveling Thief Problem.” In <i>Parallel Problem
    Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph, Anna V. Kononova,
    Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tu\v sar, 237–249. Lecture
    Notes in Computer Science. Cham: Springer International Publishing, 2022. <a href="https://doi.org/10.1007/978-3-031-14714-2_17">https://doi.org/10.1007/978-3-031-14714-2_17</a>.'
  ieee: 'A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity
    Optimisation for the Traveling Thief Problem,” in <i>Parallel Problem Solving
    from Nature (PPSN XVII)</i>, 2022, pp. 237–249, doi: <a href="https://doi.org/10.1007/978-3-031-14714-2_17">10.1007/978-3-031-14714-2_17</a>.'
  mla: Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling
    Thief Problem.” <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited
    by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249,
    doi:<a href="https://doi.org/10.1007/978-3-031-14714-2_17">10.1007/978-3-031-14714-2_17</a>.
  short: 'A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova,
    H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\v sar (Eds.), Parallel Problem Solving
    from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:51Z
department:
- _id: '819'
doi: 10.1007/978-3-031-14714-2_17
editor:
- first_name: Günter
  full_name: Rudolph, Günter
  last_name: Rudolph
- first_name: Anna V.
  full_name: Kononova, Anna V.
  last_name: Kononova
- first_name: Hernán
  full_name: Aguirre, Hernán
  last_name: Aguirre
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
- first_name: Gabriela
  full_name: Ochoa, Gabriela
  last_name: Ochoa
- first_name: Tea
  full_name: Tu\v sar, Tea
  last_name: Tu\v sar
extern: '1'
keyword:
- Co-evolutionary algorithms
- Evolutionary diversity optimisation
- Quality diversity
- Traveling thief problem
language:
- iso: eng
page: 237–249
place: Cham
publication: Parallel Problem Solving from Nature (PPSN XVII)
publication_identifier:
  isbn:
  - 978-3-031-14714-2
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
type: conference
user_id: '102979'
year: '2022'
...