On the mapping of genotype to phenotype in evolutionary algorithms

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@Article{Whigham:2017:GPEM,
  author =       "Peter A. Whigham and Grant Dick and James Maclaurin",
  title =        "On the mapping of genotype to phenotype in
                 evolutionary algorithms",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2017",
  volume =       "18",
  number =       "3",
  pages =        "353--361",
  month =        sep,
  note =         "Special Peer Commentary on Mapping of Genotype to
                 Phenotype in Evolutionary Algorithms",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Biological analogy, Representation",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-017-9288-x",
  size =         "9 pages",
  abstract =     "Analogies with molecular biology are frequently used
                 to guide the development of artificial evolutionary
                 search. A number of assumptions are made in using such
                 reasoning, chief among these is that evolution in
                 natural systems is an optimal, or at least best
                 available, search mechanism, and that a decoupling of
                 search space from behaviour encourages effective
                 search. In this paper, we explore these assumptions as
                 they relate to evolutionary algorithms, and discuss
                 philosophical foundations from which an effective
                 evolutionary search can be constructed. This framework
                 is used to examine grammatical evolution (GE), a
                 popular search method that draws heavily upon concepts
                 from molecular biology. We identify several properties
                 in GE that are in direct conflict with those that
                 promote effective evolutionary search. The paper
                 concludes with some recommendations for designing
                 representations for effective evolutionary search.",
  notes =        "See \cite{Spector:2017:GPEM} and
                 \cite{Whigham:2017:GPEM2}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9289-9
                 \cite{Whigham:2017:GPEM2}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9296-x
                 \cite{Foster:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9295-y
                 \cite{Squillero:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9294-z
                 \cite{Ryan:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9293-0
                 \cite{O'Neill:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9292-1 (Link
                 broken March 2017)

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9291-2
                 \cite{Ekart:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9290-3
                 \cite{Altenberg:2017:GPEM}

                 A comment to this article is available at
                 http://dx.doi.org/10.1007/s10710-017-9287-y
                 \cite{Spector:2017:GPEM}",
}

Genetic Programming entries for Peter Alexander Whigham Grant Dick James Maclaurin

Citations