Feature Standardisation and Coefficient Optimisation for Effective Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Dick:2020:GECCO,
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author = "Grant Dick and Caitlin A. Owen and Peter A. Whigham",
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title = "Feature Standardisation and Coefficient Optimisation
for Effective Symbolic Regression",
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year = "2020",
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editor = "Carlos Artemio {Coello Coello} and
Arturo Hernandez Aguirre and Josu Ceberio Uribe and
Mario Garza Fabre and Gregorio {Toscano Pulido} and
Katya Rodriguez-Vazquez and Elizabeth Wanner and
Nadarajen Veerapen and Efren Mezura Montes and
Richard Allmendinger and Hugo Terashima Marin and
Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and
Heike Trautmann and Ke Tang and John Koza and
Erik Goodman and William B. Langdon and Miguel Nicolau and
Christine Zarges and Vanessa Volz and Tea Tusar and
Boris Naujoks and Peter A. N. Bosman and
Darrell Whitley and Christine Solnon and Marde Helbig and
Stephane Doncieux and Dennis G. Wilson and
Francisco {Fernandez de Vega} and Luis Paquete and
Francisco Chicano and Bing Xue and Jaume Bacardit and
Sanaz Mostaghim and Jonathan Fieldsend and
Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and
Carlos Segura and Carlos Cotta and Michael Emmerich and
Mengjie Zhang and Robin Purshouse and Tapabrata Ray and
Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and
Frank Neumann",
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isbn13 = "9781450371285",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377930.3390237",
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DOI = "doi:10.1145/3377930.3390237",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference",
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pages = "306--314",
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size = "9 pages",
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keywords = "genetic algorithms, genetic programming, gradient
descent, symbolic regression, feature standardisation",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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abstract = "Symbolic regression is a common application of genetic
programming where model structure and corresponding
parameters are evolved in unison. In the majority of
work exploring symbolic regression, features are used
directly without acknowledgement of their relative
scale or unit. This paper extends recent work on the
importance of standardisation of features when
conducting symbolic regression. Specifically, z-score
standardisation of input features is applied to both
inputs and response to ensure that evolution explores a
model space with zero mean and unit variance. This
paper demonstrates that standardisation allows a
simpler function set to be used without increasing
bias. Additionally, it is demonstrated that
standardisation can significantly improve the
performance of coefficient optimisation through
gradient descent to produce accurate models. Through
analysis of several benchmark data sets, we demonstrate
that feature standardisation enables simple but
effective approaches that are comparable in performance
to the state-of-the-art in symbolic regression.",
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notes = "Also known as \cite{10.1145/3377930.3390237}
GECCO-2020 A Recombination of the 29th International
Conference on Genetic Algorithms (ICGA) and the 25th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Grant Dick
Caitlin A Owen
Peter Alexander Whigham
Citations