Symbolic Regression Model Comparison Approach Using Transmitted Variation

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Flor A. Castillo and Carlos M. Villa and 
                 Arthur K. Kordon",
  title =        "Symbolic Regression Model Comparison Approach Using
                 Transmitted Variation",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "10",
  pages =        "139--154",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, Model comparison, Transmitted variation,
                 Pareto front, Interpolation, Monte Carlo",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "",
  DOI =          "doi:10.1007/978-1-4614-6846-2_10",
  abstract =     "Model evaluation in symbolic regression generated by
                 GP is of critical importance for successful industrial
                 applications. Typically this model evaluation is
                 achieved by a tradeoff between model complexity and R
                 squared. The chapter introduces a model comparison
                 approach based on the transmission of variation from
                 the inputs to the output. The approach is illustrated
                 with three different data sets from real industrial
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",

Genetic Programming entries for Flor A Castillo Carlos Villa Arthur K Kordon