Evolutionary Data-Driven Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Nirupam Chakraborti",
  title =        "Evolutionary Data-Driven Modeling",
  booktitle =    "Informatics for Materials Science and Engineering",
  publisher =    "Butterworth-Heinemann",
  year =         "2013",
  editor =       "Krishna Rajan",
  chapter =      "5",
  pages =        "71--95",
  address =      "Oxford",
  keywords =     "genetic algorithms, genetic programming, Neural
                 network, Multi-objective optimisation, Evolutionary
  isbn13 =       "978-0-12-394399-6",
  DOI =          "doi:10.1016/B978-0-12-394399-6.00005-9",
  URL =          "http://www.sciencedirect.com/science/article/pii/B9780123943996000059",
  abstract =     "Artificial neural networks (ANNs) and genetic
                 programming (GP) have already emerged as two very
                 effective computing strategies for constructing
                 data-driven models for systems of scientific and
                 engineering interest. However, coming up with accurate
                 models or meta-models from noisy real-life data is
                 often a formidable task due to their frequent
                 association with high degrees of random noise, which
                 might render an ANN or GP model either over- or
                 underfitted. This problem has recently been tackled in
                 two emerging algorithms, Evolutionary Neural Net
                 (EvoNN) and Bi-objective Genetic Programming (BioGP),
                 which use Pareto tradeoff and apply a bi-objective
                 genetic algorithm (GA) in the basic framework of both
                 ANNs and GP.",
  notes =        "Department of Metallurgical and Materials Engineering,
                 Indian Institute of Technology, Kharagpur, India",

Genetic Programming entries for Nirupam Chakraborti