GP made faster with semantic surrogate modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Ahmed Kattan and Alexandros Agapitos and 
                 Yew-Soon Ong and Ateq A. Alghamedi and Michael O'Neill",
  title =        "{GP} made faster with semantic surrogate modelling",
  journal =      "Information Sciences",
  volume =       "355-356",
  pages =        "169--185",
  year =         "2016",
  keywords =     "genetic algorithms, genetic programming, Surrogate
                 modelling, K-NN, Symbolic regression, Classification,
                 Time-series forecasting",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2016.03.030",
  URL =          "",
  abstract =     "Genetic Programming (GP) is known to be expensive in
                 cases where the fitness evaluation is computationally
                 demanding, i.e., object detection, programmatic
                 compression, image processing applications. The paper
                 introduces a method that reduces the amount of fitness
                 evaluations that are required to obtain good solutions.
                 We consider the supervised learning setting, where a
                 training set of input vectors are collectively mapped
                 to a vector of outputs, and then a loss function is
                 used to map the vector of outputs to a scalar fitness
                 value. Saving of fitness evaluations is achieved
                 through the use of two components. The first component
                 is surrogate model that predicts trees output for a
                 particular input vector xi based on the similarity
                 between xi and other input vectors in the training set
                 for which the candidate solution has been already
                 evaluated with. The second component, is a simple
                 linear equation to control the size of a sub-training
                 set that is used to train GP trees. This linear
                 equation allows the size of the sub-training set to
                 dynamically increase or decrease based on the status of
                 the search. The proposed method referred to as SSGP.
                 Empirical results in 17 different problems, from three
                 different categories, demonstrate that SSGP is able to
                 obtain solutions of similar quality with those obtained
                 using several benchmark GP systems, but with a much
                 smaller computation time. The simplicity of the
                 proposed method and the ease of its implementation is
                 one of the most appealing aspects of its future
  notes =        "Also known as \cite{KattanINS2016}",

Genetic Programming entries for Ahmed Kattan Alexandros Agapitos Yew-Soon Ong Ateq A Alghamedi Michael O'Neill