Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming

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

@Article{Hu:2012:GPEM,
  author =       "Ting Hu and Joshua Payne and Wolfgang Banzhaf and 
                 Jason Moore",
  title =        "Evolutionary dynamics on multiple scales: a
                 quantitative analysis of the interplay between
                 genotype, phenotype, and fitness in linear genetic
                 programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "13",
  number =       "3",
  pages =        "305--337",
  month =        sep,
  note =         "Special issue on selected papers from the 2011
                 European conference on genetic programming",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 Accessibility, Coreness, Evolvability,
                 Genotype-phenotype map, Phenotype-fitness map,
                 Networks, Neutrality, Redundancy, Robustness",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-012-9159-4",
  size =         "33 pages",
  abstract =     "Redundancy is a ubiquitous feature of genetic
                 programming (GP), with many-to-one mappings commonly
                 observed between genotype and phenotype, and between
                 phenotype and fitness. If a representation is
                 redundant, then neutral mutations are possible. A
                 mutation is phenotypically-neutral if its application
                 to a genotype does not lead to a change in phenotype. A
                 mutation is fitness-neutral if its application to a
                 genotype does not lead to a change in fitness. Whether
                 such neutrality has any benefit for GP remains a
                 contentious topic, with reported experimental results
                 supporting both sides of the debate. Most existing
                 studies use performance statistics, such as success
                 rate or search efficiency, to investigate the utility
                 of neutrality in GP. Here, we take a different tack and
                 use a measure of robustness to quantify the neutrality
                 associated with each genotype, phenotype, and fitness
                 value. We argue that understanding the influence of
                 neutrality on GP requires an understanding of the
                 distributions of robustness at these three levels, and
                 of the interplay between robustness, evolvability, and
                 accessibility amongst genotypes, phenotypes, and
                 fitness values. As a concrete example, we consider a
                 simple linear genetic programming system that is
                 amenable to exhaustive enumeration and allows for the
                 full characterisation of these quantities, which we
                 then relate to the dynamical properties of simple
                 mutation-based evolutionary processes. Our results
                 demonstrate that it is not only the distribution of
                 robustness amongst phenotypes that affects evolutionary
                 search, but also (1) the distributions of robustness at
                 the genotypic and fitness levels and (2) the mutational
                 biases that exist amongst genotypes, phenotypes, and
                 fitness values. Of crucial importance is the
                 relationship between the robustness of a genotype and
                 its mutational bias toward other phenotypes.",
  notes =        "EuroGP 2011 \cite{Silva:2011:GP}",
  affiliation =  "Computational Genetics Laboratory, Dartmouth Medical
                 School, Hanover, NH, USA",
}

Genetic Programming entries for Ting Hu Joshua L Payne Wolfgang Banzhaf Jason H Moore

Citations