A Neutral Mutation Operator in Grammatical Evolution

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  author =       "Christian Oesch and Dietmar Maringer",
  title =        "A Neutral Mutation Operator in Grammatical Evolution",
  publisher =    "Springer",
  year =         "2014",
  volume =       "322",
  bibdate =      "2014-10-06",
  bibsource =    "DBLP,
  booktitle =    "IEEE Conf. on Intelligent Systems (1)",
  editor =       "Plamen P. Angelov and Krassimir T. Atanassov and 
                 Lyubka Doukovska and Mincho Hadjiski and 
                 Vladimir Simov Jotsov and Janusz Kacprzyk and Nikola Kasabov and 
                 Sotir Sotirov and Eulalia Szmidt and Slawomir Zadrozny",
  isbn13 =       "978-3-319-11312-8",
  pages =        "439--449",
  series =       "Advances in Intelligent Systems and Computing",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Neutral Evolution, Genetic Operator,
                 Genotype-Phenotype Mapping",
  URL =          "http://dx.doi.org/10.1007/978-3-319-11313-5",
  DOI =          "doi:10.1007/978-3-319-11313-5_39",
  abstract =     "n this paper we propose a Neutral Mutation Operator
                 (NMO) for Grammatical Evolution (GE). This novel
                 operator is inspired by GE's ability to create genetic
                 diversity without causing changes in the phenotype.
                 Neutral mutation happens naturally in the algorithm;
                 however, forcing such changes increases success rates
                 in symbolic regression problems profoundly with very
                 low additional CPU and memory cost. By exploiting the
                 genotype-phenotype mapping, this additional mutation
                 operator allows the algorithm to explore the search
                 space more efficiently by keeping constant genetic
                 diversity in the population which increases the
                 mutation potential. The NMO can be applied in
                 combination with any other genetic operator or even
                 different search algorithms (e.g. Differential
                 Evolution or Particle Swarm Optimization) for GE and
                 works especially well in small populations and larger

Genetic Programming entries for Christian Oesch Dietmar G Maringer