---
res:
  bibo_abstract:
  - Evolving diverse sets of high quality solutions has gained increasing interest
    in the evolutionary computation literature in recent years. With this paper, we
    contribute to this area of research by examining evolutionary diversity optimisation
    approaches for the classical Traveling Salesperson Problem (TSP). We study the
    impact of using different diversity measures for a given set of tours and the
    ability of evolutionary algorithms to obtain a diverse set of high quality solutions
    when adopting these measures. Our studies show that a large variety of diverse
    high quality tours can be achieved by using our approaches. Furthermore, we compare
    our approaches in terms of theoretical properties and the final set of tours obtained
    by the evolutionary diversity optimisation algorithm.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Anh Viet
      foaf_name: Do, Anh Viet
      foaf_surname: Do
  - foaf_Person:
      foaf_givenName: Jakob
      foaf_name: Bossek, Jakob
      foaf_surname: Bossek
      foaf_workInfoHomepage: http://www.librecat.org/personId=102979
    orcid: 0000-0002-4121-4668
  - foaf_Person:
      foaf_givenName: Aneta
      foaf_name: Neumann, Aneta
      foaf_surname: Neumann
  - foaf_Person:
      foaf_givenName: Frank
      foaf_name: Neumann, Frank
      foaf_surname: Neumann
  bibo_doi: 10.1145/3377930.3389844
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-1-4503-7128-5
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_subject:
  - diversity maximisation
  - evolutionary algorithms
  - travelling salesperson problem
  dct_title: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem@
...