Efficient Approaches to Interleaved Sampling of training data for Symbolic Regression

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

  author =       "R. Muhammad Atif Azad and David Medernach and 
                 Conor Ryan",
  title =        "Efficient Approaches to Interleaved Sampling of
                 training data for Symbolic Regression",
  booktitle =    "Sixth World Congress on Nature and Biologically
                 Inspired Computing",
  year =         "2014",
  editor =       "Ana Maria Madureira and Ajith Abraham and 
                 Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and 
                 Choo yun Huoy",
  pages =        "176--183",
  address =      "Porto, Portugal",
  month =        "30 " # jul # " - 1 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-5937-2/14",
  DOI =          "doi:10.1109/NaBIC.2014.6921874",
  abstract =     "The ability to generalise beyond the training set is
                 paramount for any machine learning algorithm and
                 Genetic Programming (GP) is no exception. This paper
                 investigates a recently proposed technique to improve
                 generalisation in GP, termed Interleaved Sampling where
                 GP alternates between using the entire data set and
                 only a single data point in alternate generations. This
                 paper proposes two alternatives to using a single data
                 point: the use of random search instead of a single
                 data point, and simply minimising the tree size. Both
                 the approaches are more efficient than the original
                 Interleaved Sampling because they simply do not
                 evaluate the fitness in half the number of generations.
                 The results show that in terms of generalisation,
                 random search and size minimisation are as effective as
                 the original Interleaved Sampling; however, they are
                 computationally more efficient in terms of data
                 processing. Size minimisation is particularly
                 interesting because it completely prevents bloat while
                 still being competitive in terms of training results as
                 well as generalisation. The tree sizes with size
                 minimisation are substantially smaller reducing the
                 computational expense substantially.",
  notes =        "NaBIC 2014 http://www.mirlabs.net/nabic14/",

Genetic Programming entries for R Muhammad Atif Azad David Medernach Conor Ryan