Parallel evolution using multi-chromosome cartesian genetic programming

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

  author =       "James Alfred Walker and Katharina Volk and 
                 Stephen L. Smith and Julian Francis Miller",
  title =        "Parallel evolution using multi-chromosome cartesian
                 genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2009",
  volume =       "10",
  number =       "4",
  pages =        "417--445",
  month =        dec,
  note =         "Special issue on parallel and distributed evolutionary
                 algorithms, part I",
  keywords =     "genetic algorithms, genetic programming, Multiple
                 chromosomes, Cartesian genetic programming, Digital
                 circuits, Mammography, Parallelisation",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-009-9093-2",
  size =         "29 pages",
  abstract =     "Parallel and distributed methods for evolutionary
                 algorithms have concentrated on maintaining multiple
                 populations of genotypes, where each genotype in a
                 population encodes a potential solution to the problem.
                 In this paper, we investigate the parallelisation of
                 the genotype itself into a collection of independent
                 chromosomes which can be evaluated in parallel. We call
                 this multi-chromosomal evolution (MCE). We test this
                 approach using Cartesian Genetic Programming and apply
                 MCE to a series of digital circuit design problems to
                 compare the efficacy of MCE with a conventional single
                 chromosome approach (SCE). MCE can be readily used for
                 many digital circuits because they have multiple
                 outputs. In MCE, an independent chromosome is assigned
                 to each output. When we compare MCE with SCE we find
                 that MCE allows us to evolve solutions much faster. In
                 addition, in some cases we were able to evolve
                 solutions with MCE that we unable to with SCE. In a
                 case-study, we investigate how MCE can be applied to to
                 a single objective problem in the domain of image
                 classification, namely, the classification of breast
                 X-rays for cancer. To apply MCE to this problem, we
                 identify regions of interest (RoI) from the mammograms,
                 divide the RoI into a collection of sub-images and use
                 a chromosome to classify each sub-image. This problem
                 allows us to evaluate various evolutionary mutation
                 operators which can pairwise swap chromosomes either
                 randomly or topographically or reuse chromosomes in
                 place of other chromosomes.",
  notes =        "computational effort. p443 'multi-chromosomes as
                 population members, where chromosomes can be evaluated

Genetic Programming entries for James Alfred Walker Katharina Volk Stephen L Smith Julian F Miller