Online Discovery of Search Objectives for Test-Based Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Pawel Liskowski and Krzysztof Krawiec",
  title =        "Online Discovery of Search Objectives for Test-Based
  journal =      "Evolutionary Computation",
  year =         "2017",
  volume =       "25",
  number =       "3",
  pages =        "375--406",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming, Coevolution,
                 test-based problems, multi-objective evolutionary
                 computation, search driver",
  ISSN =         "1063-6560",
  URL =          "",
  DOI =          "doi:10.1162/evco_a_00179",
  size =         "32 pages",
  abstract =     "In test-based problems, commonly approached with
                 competitive coevolutionary algorithms, the fitness of a
                 candidate solution is determined by the outcomes of its
                 interactions with multiple tests. Usually, fitness is a
                 scalar aggregate of interaction outcomes, and as such
                 imposes a complete order on the candidate solutions.
                 However, passing different tests may require unrelated
                 skills, and candidate solutions may vary with respect
                 to such capabilities. In this study, we provide
                 theoretical evidence that scalar fitness, inherently
                 incapable of capturing such differences, is likely to
                 lead to premature convergence. To mitigate this
                 problem, we propose disco, a method that automatically
                 identifies the groups of tests for which the candidate
                 solutions behave similarly and define the above skills.
                 Each such group gives rise to a derived objective, and
                 these objectives together guide the search algorithm in
                 multi-objective fashion. When applied to several
                 well-known test-based problems, the proposed approach
                 significantly outperforms the conventional
                 two-population coevolution. This opens the door to
                 efficient and generic countermeasures to premature
                 convergence for both coevolutionary and evolutionary
                 algorithms applied to problems featuring aggregating
                 fitness functions.",
  notes =        "PMID: 26953882",

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec