Improving Generalisation of Genetic Programming for High-Dimensional Symbolic Regression with Feature Selection

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

@InProceedings{Chen:2016:CEC,
  author =       "Qi Chen and Bing Xue and Ben Niu and Mengjie Zhang",
  title =        "Improving Generalisation of Genetic Programming for
                 High-Dimensional Symbolic Regression with Feature
                 Selection",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3793--3800",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744270",
  abstract =     "Feature selection is a desired process when learning
                 from high-dimensional data. However, it is seldom
                 considered in Genetic Programming (GP) for
                 high-dimensional symbolic regression. This work aims to
                 develop a new method, Genetic Programming with Feature
                 Selection (GPWFS), to improve the generalisation
                 ability of GP for symbolic regression. GPWFS is a
                 two-stage method. The main task of the first stage is
                 to select important/informative features from fittest
                 individuals, and the second stage uses a set of
                 selected features, which is a subset of original
                 features, for regression. To investigate the
                 learning/optimisation performance and generalisation
                 capability of GPWFS, a set of experiments using
                 standard GP as a baseline for comparison have been
                 conducted on six real-world high-dimensional symbolic
                 regression datasets. The experimental results show that
                 GPWFS can have better performance both on the training
                 sets and the test sets on most cases. Further analysis
                 on the solution size, the number of distinguished
                 features and total number of used features in the
                 evolved models shows that using GPWFS can induce more
                 compact models with better interpretability and lower
                 computational costs than standard GP.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Qi Chen Bing Xue Ben Niu Mengjie Zhang

Citations