Feature fitness evaluation for symbolic regression via genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Lu:2011:ICNC,
  author =       "Qiang Lu and Bin Wang",
  title =        "Feature fitness evaluation for symbolic regression via
                 genetic programming",
  booktitle =    "Seventh International Conference on Natural
                 Computation (ICNC 2011)",
  year =         "2011",
  month =        "26-28 " # jul,
  volume =       "2",
  pages =        "1087--1091",
  address =      "Shanghai",
  size =         "5 pages",
  abstract =     "In this paper, feature fitness evaluation method is
                 proposed for accelerating the speed of evolution in
                 symbolic regression. Through analysing the feature of
                 curve or surface which train data represents, vertex
                 and inflection points are extracted from the train
                 data. According to the feature data and diversity of
                 population, the test data for evolution of genetic
                 programming (GP) are generated dynamically. The method
                 was implemented by using GP and genetic expression
                 programming(GEP). Results show that the method in GP,
                 compared with classic GP and GEP, has benefits about
                 efficient of computation, regression performance and
                 avoiding premature convergence.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP, data representation,
                 feature fitness evaluation, genetic expression
                 programming, inflection points, symbolic regression,
                 vertex points, regression analysis",
  DOI =          "doi:10.1109/ICNC.2011.6022150",
  ISSN =         "2157-9555",
  notes =        "Also known as \cite{6022150}",
}

Genetic Programming entries for Qiang Lu Bin Wang

Citations