Fitness landscape analysis for evolutionary non-photorealistic rendering

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Riley:2010:cec,
  author =       "Jeff Riley and Vic Ciesielski",
  title =        "Fitness landscape analysis for evolutionary
                 non-photorealistic rendering",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "The best evolutionary approach can be a difficult
                 problem. In this work we have investigated two
                 evolutionary representations to evolve
                 non-photorealistic renderings: a variable-length
                 classic genetic algorithm representation, and a
                 tree-based genetic algorithm representation. These
                 representations exhibit very different convergence
                 behaviour, and despite considerable exploration of
                 parameters the classic genetic algorithm was not
                 competitive with the tree-based approach for the
                 problem studied in this work. The aim of the work
                 presented in this paper was to investigate whether
                 analysis of the fitness landscapes described by the
                 different representations can explain the difference in
                 performance. We used several current fitness landscape
                 measures to analyse the fitness landscapes, and found
                 that one of the measures suggests there is a
                 correlation between search performance and the fitness
                 landscape.",
  DOI =          "doi:10.1109/CEC.2010.5586013",
  notes =        "WCCI 2010. Also known as \cite{5586013}",
}

Genetic Programming entries for Jeff Riley Victor Ciesielski

Citations