Feature Extraction for Surrogate Models in Genetic Programming

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

  author =       "Martin Pilat and Roman Neruda",
  title =        "Feature Extraction for Surrogate Models in Genetic
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "335--344",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Surrogate
                 model, Random forest",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_31",
  abstract =     "We discuss the use of surrogate models in the field of
                 genetic programming. We describe a set of features
                 extracted from each tree and use it to train a model of
                 the fitness function. The results indicate that such a
                 model can be used to predict the fitness of new
                 individuals without the need to evaluate them. In a
                 series of experiments, we show how surrogate modelling
                 is able to reduce the number of fitness evaluations
                 needed in genetic programming, and we discuss how the
                 use of surrogate models affects the exploration and
                 convergence of genetic programming algorithms.",
  notes =        "PPSN2016 http://ppsn2016.org",

Genetic Programming entries for Martin Pilat Roman Neruda