Quantum-Inspired Multi-gene Linear Genetic Programming Model for Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Strachan:2014:BRACIS,

• author = "Guilherme C. Strachan and Adriano S. Koshiyama and Douglas M. Dias and Marley M. B. R. Vellasco and Marco A. C. Pacheco",
• booktitle = "Brazilian Conference on Intelligent Systems (BRACIS 2014)",
• title = "Quantum-Inspired Multi-gene Linear Genetic Programming Model for Regression Problems",
• year = "2014",
• month = oct,
• pages = "152--157",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/BRACIS.2014.37",
• size = "6 pages",
• abstract = "We propose the Quantum-Inspired Multi-Gene Linear Genetic Programming (QIMuLGP), which is a generalisation of Quantum-Inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming, Multi-Gene tree-based GP (MGGP), and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its run time always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.",
• notes = "Also known as \cite{6984823}",

}

Genetic Programming entries for Guilherme Cesario Strachan Adriano Soares Koshiyama Douglas Mota Dias Marley Maria Bernardes Rebuzzi Vellasco Marco Aurelio Cavalcanti Pacheco

Citations