An Individually Variable Mutation-Rate Strategy for Genetic Algorithms

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@InProceedings{stanhope:1997:ivm-r,
  author =       "Stephen A. Stanhope and Jason M. Daida",
  title =        "An Individually Variable Mutation-Rate Strategy for
                 Genetic Algorithms",
  booktitle =    "Evolutionary Programming VI: Proceedings of the Sixth
                 Annual Conference on Evolutionary Programming",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and 
                 John R. McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  pages =        "235--245",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  month =        apr # " 13-16",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-62788-3",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/EP97mutation.pdf",
  DOI =          "doi:10.1007/BFb0014815",
  size =         "11 pages",
  abstract =     "In Neo-Darwinism, mutation can be considered to be
                 unaffected by selection pressure. This is the metaphor
                 generally used by the genetic algorithm for its
                 treatment of the mutation operation, which is usually
                 regarded as a background operator. This metaphor,
                 however, does not take into account the fact that
                 mutation has been shown to be affected by external
                 events. In this paper, we propose a mutation-rate
                 strategy that is variable between individuals within a
                 given generation based on the individual's relative
                 performance for the purpose of function optimisation.",
  notes =        "EP-97",
}

Genetic Programming entries for Stephen A Stanhope Jason M Daida

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