Genotype-Phenotype-Mapping and Neutral Variation -- A case study in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Wolfgang Banzhaf",
  title =        "Genotype-Phenotype-Mapping and Neutral Variation -- A
                 case study in Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and 
                 Reinhard M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "322--332",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-540-58484-6_276",
  size =         "10 pages",
  abstract =     "We propose the application of a genotype-phenotype
                 mapping to the solution of constrained optimization
                 problems. The method consists of strictly separating
                 the search space of genotypes from the solution space
                 of phenotypes. A mapping from genotypes into phenotypes
                 provides for the appropriate expression of information
                 represented by the genotypes. The mapping is
                 constructed as to guarantee feasibility of phenotypic
                 solutions for the problem under study. This enforcing
                 of constraints causes multiple genotypes to result in
                 one and the same phenotype. Neutral variants are
                 therefore frequent and play an important role in
                 maintaining genetic diversity. As a specific example,
                 we discuss Binary Genetic Programming (BGP), a variant
                 of Genetic Programming that uses binary strings as
                 genotypes and program trees as phenotypes.",
  notes =        "PPSN3

                 Tested on symbolic regression of 0.5x**2 and
                 exp(-3.0*x**2) Produces high level code (FORTRAN, C?)
                 which is compiled, claims this gives huge speedup.


Genetic Programming entries for Wolfgang Banzhaf