Multiple regression genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Arnaldo:2014:GECCO,

• author = "Ignacio Arnaldo and Krzysztof Krawiec and Una-May O'Reilly",
• title = "Multiple regression genetic programming",
• booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",
• year = "2014",
• 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",
• isbn13 = "978-1-4503-2662-9",
• pages = "879--886",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, MRGP, Multiple Regression",
• URL = "http://doi.acm.org/10.1145/2576768.2598291",
• DOI = "doi:10.1145/2576768.2598291",
• code_url = "https://flexgp.github.io/gp-learners/mrgp.html",
• size = "8 pages",
• 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.",
• 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