A Hybrid Grammar-based Genetic Programming for Symbolic Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Motta:2018:CEC,

• author = "Flavio Motta and Joao Freitas and Felipe Souza and Heder Bernardino and Itamar Oliveira and Helio Barbosa",
• title = "A Hybrid Grammar-based Genetic Programming for Symbolic Regression Problems",
• booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2018",
• editor = "Marley Vellasco",
• address = "Rio de Janeiro, Brazil",
• month = "8-13 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2018.8477826",
• 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.",
• 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