Defining Locality in Genetic Programming to Predict Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Edgar Galvan-Lopez and James McDermott and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "Defining Locality in Genetic Programming to Predict
  booktitle =    "2010 IEEE World Congress on Computational
  pages =        "1828--1835",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-6910-9",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2010.5586095",
  abstract =     "A key indicator of problem difficulty in evolutionary
                 computation problems is the landscape's locality, that
                 is whether the genotype-phenotype mapping preserves
                 neighbourhood. In genetic programming the genotype and
                 phenotype are not distinct, but the locality of the
                 genotypefitness mapping is of interest. In this paper
                 we extend the original standard quantitative definition
                 of locality to cover the genotype-fitness case,
                 considering three possible definitions. By relating the
                 values given by these definitions with the results of
                 evolutionary runs, we investigate which definition is
                 the most useful as a predictor of performance.",
  notes =        "WCCI 2010. Also known as \cite{5586095}",

Genetic Programming entries for Edgar Galvan Lopez James McDermott Michael O'Neill Anthony Brabazon