Examining the use of a Non-Trivial Fixed Genotype-Phenotype Mapping in Genetic Algorithms to Induce Phenotypic Variability over Deceptive Uncertain Landscapes

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

@InProceedings{Hill:2011:EtuoaNFGMiGAtIPVoDUL,
  title =        "Examining the use of a Non-Trivial Fixed
                 Genotype-Phenotype Mapping in Genetic Algorithms to
                 Induce Phenotypic Variability over Deceptive Uncertain
                 Landscapes",
  author =       "Seamus Hill and Colm O'Riordan",
  pages =        "1404--1411",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Representation and operators",
  DOI =          "doi:10.1109/CEC.2011.5949780",
  abstract =     "In nature, living organisms can be viewed as the
                 product of their genotype-phenotype mapping (GP-map).
                 This paper presents a GP-map loosely based on the
                 biological phenomena of transcription and translation,
                 to create a multi-layered GP-map which increases the
                 level of phenotypic variability. The aim of the paper
                 is to examine through the use of a fixed non-trivial
                 GP-map, the impact of increased phenotypic variability,
                 on search over a set of deceptive landscapes. The
                 GP-map allows for a non-injective genotype-phenotype
                 relationship, and the phenotypic variability of a
                 number of phenotypes, introduced by the GP-map, are
                 advanced from the genotypes used to encode them through
                 a basic interpretation of transcription and
                 translation. We attempt to analyse the level of
                 variability by measuring diversity, both at a genotypic
                 and phenotypic level. The multi-layered GP-map is
                 incorporated into a Genetic Algorithm, the
                 multi-layered mapping GA (MMGA), and runs over a number
                 of GA-Hard landscapes. Initial empirical results appear
                 to indicate that over deceptive landscapes, as the
                 level of problem difficulty increases, so too does the
                 benefit of using the proposed GP-map to probe the
                 search space.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for Seamus Hill Colm O'Riordan

Citations