Site Characterization Using GP, MARS and GPR

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Pijush Samui and Yildirim Dalkilic and J. Jagan",
  title =        "Site Characterization Using GP, MARS and GPR",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "13",
  pages =        "345--357",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_13",
  abstract =     "This article examines the capability of Genetic
                 Programming (GP), Multivariate Adaptive Regression
                 Spline (MARS) and Gaussian Process Regression (GPR) for
                 developing site characterization model of Bangalore
                 (India) based on corrected Standard Penetration Test
                 (SPT) value (Nc). GP, MARS and GPR have been used as
                 regression techniques. GP is developed based on genetic
                 algorithm. MARS does not assume any functional
                 relationship between input and output variables. GPR is
                 a probabilistic, non-parametric model. In GPR,
                 different kinds of prior knowledge can be applied. In
                 three dimensional analysis, the function Nc=f(X,Y,Z)
                 where X, Y and Z are the coordinates of a point
                 corresponding to N value, is to be approximated with
                 which N value at any half space point in Bangalore can
                 be determined. A comparative study between the
                 developed GP, MARS and GPR has been carried out in the
                 proposed book chapter. The developed GP, MARS and GPR
                 give the spatial variability of Nc values at

Genetic Programming entries for Pijush Samui Yildirim Dalkilic J Jagan