A Neutral Mutation Operator in Grammatical Evolution
Created by W.Langdon from
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- @InProceedings{conf/is/OeschM14,
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author = "Christian Oesch and Dietmar Maringer",
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title = "A Neutral Mutation Operator in Grammatical Evolution",
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publisher = "Springer",
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year = "2014",
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volume = "322",
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bibdate = "2014-10-06",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/is/is2014-1.html#OeschM14",
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booktitle = "IEEE Conf. on Intelligent Systems (1)",
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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",
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isbn13 = "978-3-319-11312-8",
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pages = "439--449",
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series = "Advances in Intelligent Systems and Computing",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, Neutral Evolution, Genetic Operator,
Genotype-Phenotype Mapping",
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URL = "http://dx.doi.org/10.1007/978-3-319-11313-5",
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DOI = "doi:10.1007/978-3-319-11313-5_39",
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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
problems.",
- }
Genetic Programming entries for
Christian Oesch
Dietmar G Maringer
Citations