The effects of recombination on phenotypic exploration and robustness in evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ting Hu and Wolfgang Banzhaf and Jason H. Moore",
  journal =      "Artificial Life",
  title =        "The effects of recombination on phenotypic exploration
                 and robustness in evolution",
  year =         "2014",
  month =        oct,
  volume =       "20",
  number =       "4",
  pages =        "457--470",
  note =         "Ten thousandth GP entry in the genetic programming
  keywords =     "genetic algorithms, genetic programming,
                 Recombination, epistasis, evolvability, genotype
                 network, robustness",
  DOI =          "doi:10.1162/ARTL_a_00145",
  ISSN =         "1064-5462",
  abstract =     "Recombination is a commonly used genetic operator in
                 artificial and computational evolutionary systems. It
                 has been empirically shown to be essential for
                 evolutionary processes. However, little has been done
                 to analyse the effects of recombination on quantitative
                 genotypic and phenotypic properties. The majority of
                 studies only consider mutation, mainly due to the more
                 serious consequences of recombination in reorganising
                 entire genomes. Here we adopt methods from evolutionary
                 biology to analyse a simple, yet representative,
                 genetic programming method, linear genetic programming.
                 We demonstrate that recombination has less disruptive
                 effects on phenotype than mutation, that it accelerates
                 novel phenotypic exploration, and that it particularly
                 promotes robust phenotypes and evolves genotypic
                 robustness and synergistic epistasis. Our results
                 corroborate an explanation for the prevalence of
                 recombination in complex living organisms, and helps
                 elucidate a better understanding of the evolutionary
                 mechanisms involved in the design of complex artificial
                 evolutionary systems and intelligent algorithms.",
  notes =        "Also known as \cite{6926028}",

Genetic Programming entries for Ting Hu Wolfgang Banzhaf Jason H Moore