Genetic Programming using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes   [GP] [GM]

by

Keller, R., E. and Banzhaf, W.

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Info: Genetic Programming 1996: Proceedings of the First Annual Conference (Conference proceedings), 1996, p. 116-122
Keywords:Genetic Programming, Genetic Algorithms
Abstract:
In common genetic programming [GP] approaches, the space of genotypes, that is the search space, [SS] is identical to the space of phenotypes, that is the solution space. Facts and theories from molecular biology suggest the introduction of non-identical genospaces and phenospaces, and a generic genotype-phenotype mapping [GM] which maps unconstrained genotypes into syntactically correct phenotypes. Neutral variants come into effect due to this mapping. They enhance genetic diversity [GD] and allow for escaping local optima in phenospace via high-dimensional saddle surfaces in genospace. We propose a concrete mapping that maps linear binary genotypes into linear phenotypes of an arbitrary context-free programming language. Empirical results are presented which show that the mapping improves the performance of GP under mutation and reproduction.
Notes:
GP-96
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BibTex:
@InProceedings{keller:1996:gpmlg2lp,
  author =       "Robert E. Keller and Wolfgang Banzhaf",
  title =        "Genetic Programming using Genotype-Phenotype Mapping
                 from Linear Genomes into Linear Phenotypes",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "116--122",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  abstract =     "

                 In common genetic programming approaches, the space of
                 genotypes, that is the search space, is identical to
                 the space of phenotypes, that is the solution space.
                 Facts and theories from molecular biology suggest the
                 introduction of non-identical genospaces and
                 phenospaces, and a generic genotype-phenotype mapping
                 which maps unconstrained genotypes into syntactically
                 correct phenotypes. Neutral variants come into effect
                 due to this mapping. They enhance genetic diversity and
                 allow for escaping local optima in phenospace via
                 high-dimensional saddle surfaces in genospace. We
                 propose a concrete mapping that maps linear binary
                 genotypes into linear phenotypes of an arbitrary
                 context-free programming language. Empirical results
                 are presented which show that the mapping improves the
                 performance of GP under mutation and reproduction.",
  notes =        "GP-96",
}