Multiple regression genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Arnaldo:2014:GECCO,
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author = "Ignacio Arnaldo and Krzysztof Krawiec and
Una-May O'Reilly",
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title = "Multiple regression genetic programming",
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booktitle = "GECCO '14: Proceedings of the 2014 conference on
Genetic and evolutionary computation",
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year = "2014",
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editor = "Christian Igel and Dirk V. Arnold and
Christian Gagne and Elena Popovici and Anne Auger and
Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and
Kalyanmoy Deb and Benjamin Doerr and James Foster and
Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and
Hitoshi Iba and Christian Jacob and Thomas Jansen and
Yaochu Jin and Marouane Kessentini and
Joshua D. Knowles and William B. Langdon and Pedro Larranaga and
Sean Luke and Gabriel Luque and John A. W. McCall and
Marco A. {Montes de Oca} and Alison Motsinger-Reif and
Yew Soon Ong and Michael Palmer and
Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and
Guenther Ruhe and Tom Schaul and Thomas Schmickl and
Bernhard Sendhoff and Kenneth O. Stanley and
Thomas Stuetzle and Dirk Thierens and Julian Togelius and
Carsten Witt and Christine Zarges",
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isbn13 = "978-1-4503-2662-9",
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pages = "879--886",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Vancouver, BC, Canada",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, MRGP,
Multiple Regression",
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URL = "http://doi.acm.org/10.1145/2576768.2598291",
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DOI = "doi:10.1145/2576768.2598291",
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code_url = "https://flexgp.github.io/gp-learners/mrgp.html",
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size = "8 pages",
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abstract = "We propose a new means of executing a genetic program
which improves its output quality. Our approach, called
Multiple Regression Genetic Programming (MRGP)
decouples and linearly combines a program's
subexpressions via multiple regression on the target
variable. The regression yields an alternate output:
the prediction of the resulting multiple regression
model. It is this output, over many fitness cases, that
we assess for fitness, rather than the program's
execution output. MRGP can be used to improve the
fitness of a final evolved solution. On our
experimental suite, MRGP consistently generated
solutions fitter than the result of competent GP or
multiple regression. When integrated into GP, inline
MRGP, on the basis of equivalent computational budget,
outperforms competent GP while also besting post-run
MRGP. Thus MRGP's output method is shown to be superior
to the output of program execution and it represents a
practical, cost neutral, improvement to GP.",
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notes = "Also known as \cite{2598291} GECCO-2014 A joint
meeting of the twenty third international conference on
genetic algorithms (ICGA-2014) and the nineteenth
annual genetic programming conference (GP-2014)",
- }
Genetic Programming entries for
Ignacio Arnaldo Lucas
Krzysztof Krawiec
Una-May O'Reilly
Citations