A Hybrid Grammar-based Genetic Programming for Symbolic Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Motta:2018:CEC,
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author = "Flavio Motta and Joao Freitas and Felipe Souza and
Heder Bernardino and Itamar Oliveira and
Helio Barbosa",
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title = "A Hybrid Grammar-based Genetic Programming for
Symbolic Regression Problems",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2018.8477826",
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abstract = "Genetic Programming (GP) is an important technique in
evolutionary computing. There has been extensive
research and great achievement in GP and its variants.
Grammar-based genetic programming (GGP) is one of the
most promising ones. We propose here a hybrid approach
of GGP with Evolution Strategies (ES). GGP is used to
evolve the structure of the models while ES searches
for the numerical coefficients in order to improve the
overall performance when solving symbolic regression
problems. Computational experiments conducted on a set
of test-cases reveal that the proposed hybrid approach
achieved a good performance when compared to other
methods from the literature.",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Flavio Andrade Amaral Motta
Joao Marcos de Freitas
Felipe Souza Teixeira
Heder Soares Bernardino
Itamar Leite de Oliveira
Helio J C Barbosa
Citations