Building Predictive Models via Feature Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Arnaldo:2015:GECCO,

• author = "Ignacio Arnaldo and Una-May O'Reilly and Kalyan Veeramachaneni",
• title = "Building Predictive Models via Feature Synthesis",
• booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• 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",
• isbn13 = "978-1-4503-3472-3",
• pages = "983--990",
• keywords = "genetic algorithms, genetic programming",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739480.2754693",
• DOI = "10.1145/2739480.2754693",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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.",
• 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