Latent Variable Symbolic Regression for High-Dimensional Inputs

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Trent McConaghy",
  title =        "Latent Variable Symbolic Regression for
                 High-Dimensional Inputs",
  booktitle =    "Genetic Programming Theory and Practice {VII}",
  year =         "2009",
  editor =       "Rick L. Riolo and Una-May O'Reilly and 
                 Trent McConaghy",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor",
  month =        "14-16 " # may,
  publisher =    "Springer",
  chapter =      "7",
  pages =        "103--118",
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, latent variables, latent variable
                 regression, LVR, analog, integrated circuits",
  isbn13 =       "978-1-4419-1653-2",
  DOI =          "doi:10.1007/978-1-4419-1626-6_7",
  URL =          "",
  size =         "17 pages",
  abstract =     "This paper explores symbolic regression when there are
                 hundreds of input variables, and the variables have
                 similar influence which means that variable pruning (a
                 priori, or on-the-fly) will be ineffective. For this
                 problem, traditional genetic programming and many other
                 regression approaches do poorly. We develop a technique
                 based on latent variables, nonlinear sensitivity
                 analysis, and genetic programming designed to manage
                 the challenge. The technique handles 340- input
                 variable problems in minutes, with promise to scale
                 well to even higher dimensions. The technique is
                 successfully verified on 24 real-world circuit
                 modelling problems",
  notes =        "part of \cite{Riolo:2009:GPTP}",

Genetic Programming entries for Trent McConaghy