Building Predictive Models via Feature Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Arnaldo:2015:GECCO,
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author = "Ignacio Arnaldo and Una-May O'Reilly and
Kalyan Veeramachaneni",
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title = "Building Predictive Models via Feature Synthesis",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "983--990",
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keywords = "genetic algorithms, genetic programming",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754693",
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DOI = "doi:10.1145/2739480.2754693",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "We introduce Evolutionary Feature Synthesis (EFS), a
regression method that generates readable, nonlinear
models of small to medium size datasets in seconds. EFS
is, to the best of our knowledge, the fastest
regression tool based on evolutionary computation
reported to date. The feature search involved in the
proposed method is composed of two main steps: feature
composition and feature subset selection. EFS adopts a
bottom-up feature composition strategy that eliminates
the need for a symbolic representation of the features
and exploits the variable selection process involved in
pathwise regularized linear regression to perform the
feature subset selection step. The result is a
regression method that is competitive against neural
networks, and outperforms both linear methods and
Multiple Regression Genetic Programming, up to now the
best regression tool based on evolutionary
computation.",
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notes = "Also known as \cite{2754693} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Ignacio Arnaldo Lucas
Una-May O'Reilly
Kalyan Veeramachaneni
Citations