Multiple regression genetic programming

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@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",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598291",
  DOI =          "doi:10.1145/2576768.2598291",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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