Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings

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

  author =       "Garnett Wilson and Malcolm Heywood",
  title =        "Introducing probabilistic adaptive mapping
                 developmental genetic programming with redundant
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2007",
  volume =       "8",
  number =       "2",
  pages =        "187--220",
  month =        jun,
  note =         "Special issue on developmental systems",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-007-9027-9",
  size =         "34 pages",
  abstract =     "Developmental Genetic Programming (DGP) algorithms
                 have explicitly required the search space for a problem
                 to be divided into genotypes and corresponding
                 phenotypes. The two search spaces are often connected
                 with a genotype-phenotype mapping (GPM) intended to
                 model the biological genetic code, where current
                 implementations of this concept involve evolution of
                 the mappings along with evolution of the genotype
                 solutions. This work presents the Probabilistic
                 Adaptive Mapping DGP (PAM DGP), a new developmental
                 implementation that involves research contributions in
                 the areas of GPMs and coevolution. The algorithm
                 component of PAM DGP is demonstrated to overcome
                 coevolutionary performance problems that are identified
                 and empirically benchmarked against the latest
                 competing algorithm that adapts similar GPMs. An
                 adaptive redundant mapping encoding is then
                 incorporated into PAM DGP for further performance
                 enhancement. PAM DGP with two mapping types are
                 compared to the competing Adaptive Mapping algorithm
                 and Traditional GP in two medical classification
                 domains, where PAM DGP with redundant encodings is
                 found to provide superior fitness performance over the
                 other algorithms through its ability to explicitly
                 decrease the size of the function set during

Genetic Programming entries for Garnett Carl Wilson Malcolm Heywood