FFX: Fast, Scalable, Deterministic Symbolic Regression Technology

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{McConaghy:2011:GPTP,
  author =       "Trent McConaghy",
  title =        "FFX: Fast, Scalable, Deterministic Symbolic Regression
                 Technology",
  booktitle =    "Genetic Programming Theory and Practice IX",
  year =         "2011",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Jason H. Moore",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  publisher =    "Springer",
  chapter =      "13",
  pages =        "235--260",
  keywords =     "genetic algorithms, genetic programming, technology,
                 symbolic regression, pathwise, regularisation,
                 real-world problems, machine learning, lasso, ridge
                 regression, elastic net, integrated circuits",
  isbn13 =       "978-1-4614-1769-9",
  URL =          "http://trent.st/content/2011-GPTP-FFX-paper.pdf",
  DOI =          "doi:10.1007/978-1-4614-1770-5_13",
  size =         "27 pages",
  abstract =     "Symbolic regression is a common application for
                 genetic programming (GP). we present a new
                 non-evolutionary technique for symbolic regression
                 that, compared to competent GP approaches on real-world
                 problems, is orders of magnitude faster (taking just
                 seconds), returns simpler models, has comparable or
                 better prediction on unseen data, and converges
                 reliably and deterministically. I dub the approach FFX,
                 for Fast Function Extraction. FFX uses a recently
                 developed machine learning technique, pathwise
                 regularised learning, to rapidly prune a huge set of
                 candidate basis functions down to compact models. FFX
                 is verified on a broad set of real-world problems
                 having 13 to 1468 input variables, outperforming GP as
                 well as several state-of-the-art regression
                 techniques.",
  notes =        "part of \cite{Riolo:2011:GPTP}",
  affiliation =  "Solido Design Automation Inc., Saskatoon, Canada",
}

Genetic Programming entries for Trent McConaghy

Citations