Quantum-Inspired Multi-gene Linear Genetic Programming Model for Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Strachan:2014:BRACIS,
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author = "Guilherme C. Strachan and Adriano S. Koshiyama and
Douglas M. Dias and Marley M. B. R. Vellasco and
Marco A. C. Pacheco",
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booktitle = "Brazilian Conference on Intelligent Systems (BRACIS
2014)",
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title = "Quantum-Inspired Multi-gene Linear Genetic Programming
Model for Regression Problems",
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year = "2014",
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month = oct,
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pages = "152--157",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/BRACIS.2014.37",
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size = "6 pages",
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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