Semantics based Mutation in Genetic Programming: The case for Real-valued Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Nguyen:2009:MENDEL,
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author = "Quang Uy Nguyen and Xuan Hoai Nguyen and
Michael O'Neill",
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title = "Semantics based Mutation in Genetic Programming: The
case for Real-valued Symbolic Regression",
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booktitle = "15th International Conference on Soft Computing,
Mendel'09",
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year = "2009",
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editor = "R. Matousek and L. Nolle",
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pages = "73--91",
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address = "Brno, Czech Republic",
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month = jun # " 24-26",
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email = "quanguyhn@yahoo.com",
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keywords = "genetic algorithms, genetic programming, Semantics,
Mutation Operator, Symbolic Regression",
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isbn13 = "978-80-214-3884-2",
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URL = "http://ncra.ucd.ie/papers/mendel2009SSM.pdf",
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size = "8 pages",
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abstract = "In this paper we propose two new methods for
implementing the mutation operator in Genetic
Programming called Semantic Aware Mutation (SAM) and
Semantic Similarity based Mutation (SSM). SAM is
inspired by our previous work on a semantics based
crossover called Semantic Aware Crossover (SAC) [19]
and SSM is an extension of SAM by adding more control
on the change of semantics of the subtrees involved in
mutation operation. We apply these two new mutation
operators to a class of real-valued symbolic regression
problems and compare them with the Standard Mutation
(SM) of Koza [13]. The results from the experiments
show that while SAM does not help to improve the
performance of Genetic Programming, SSM helps to
significantly enhance Genetic Programming performance
on the problems tried. The experiment results also show
that the change of the semantics (fitness) in SSM is
smoother than ones of both SAM and SM. This, we argue
that is the main reason to the significant performance
improvement of SSM over SAM and SC.",
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notes = "http://www.mendel-conference.org/ ID09051 Also in
electronic form ISSN 1803-3814",
- }
Genetic Programming entries for
Quang Uy Nguyen
Nguyen Xuan Hoai
Michael O'Neill
Citations