---
res:
  bibo_abstract:
  - 'Generating new instances via evolutionary methods is commonly used to create
    new benchmarking data-sets, with a focus on attempting to cover an instance-space
    as completely as possible. Recent approaches have exploited Quality-Diversity
    methods to evolve sets of instances that are both diverse and discriminatory with
    respect to a portfolio of solvers, but these methods can be challenging when attempting
    to find diversity in a high-dimensional feature-space. We address this issue by
    training a model based on Principal Component Analysis on existing instances to
    create a low-dimension projection of the high-dimension feature-vectors, and then
    apply Novelty Search directly in the new low-dimension space. We conduct experiments
    to evolve diverse and discriminatory instances of Knapsack Problems, comparing
    the use of Novelty Search in the original feature-space to using Novelty Search
    in a low-dimensional projection, and repeat over a given set of dimensions. We
    find that the methods are complementary: if treated as an ensemble, they collectively
    provide increased coverage of the space. Specifically, searching for novelty in
    a low-dimension space contributes 56% of the filled regions of the space, while
    searching directly in the feature-space covers the remaining 44%.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alejandro
      foaf_name: Marrero, Alejandro
      foaf_surname: Marrero
  - foaf_Person:
      foaf_givenName: Eduardo
      foaf_name: Segredo, Eduardo
      foaf_surname: Segredo
  - foaf_Person:
      foaf_givenName: Emma
      foaf_name: Hart, Emma
      foaf_surname: Hart
  - 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
  bibo_doi: 10.1145/3583131.3590504
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9798400701191
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_subject:
  - evolutionary computation
  - instance generation
  - instance-space analysis
  - knapsack problem
  - novelty search
  dct_title: Generating Diverse and Discriminatory Knapsack Instances by Searching
    for Novelty in Variable Dimensions of Feature-Space@
...