Cartesian Genetic Programming Based Optimization and Prediction

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

  author =       "Kisung Seo and Byeongyong Hyeon",
  title =        "Cartesian Genetic Programming Based Optimization and
  booktitle =    "WorldCIST 2014",
  year =         "2014",
  editor =       "Alvaro Rocha and Ana Maria Correia and 
                 Felix . B Tan and Karl . A Stroetmann",
  volume =       "275",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "497--502",
  address =      "Madeira Island, Portugal",
  month =        apr # " 15-18",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, gait optimisation, heavy rain
                 prediction, symbolic regression",
  bibdate =      "2015-02-04",
  bibsource =    "DBLP,
  language =     "English",
  isbn13 =       "978-3-319-05950-1",
  DOI =          "doi:10.1007/978-3-319-05951-8_47",
  abstract =     "This paper introduces a CGP (Cartesian Genetic
                 Programming) based optimisation and prediction
                 techniques. In order to provide a superior search for
                 optimisation and a robust model for prediction, a
                 nonlinear and symbolic regression method using CGP is
                 suggested. CGP uses as genotype a linear string of
                 integers that are mapped to a directed graph.
                 Therefore, some evolved modules for regression
                 polynomials in CGP network can be shared and reused
                 among multiple outputs for prediction of neighbourhood
                 precipitation. To investigate the effectiveness of the
                 proposed approach, experiments on gait generation for
                 quadruped robots and prediction of heavy precipitation
                 for local area of Korean Peninsular were executed.",

Genetic Programming entries for Kisung Seo Byeongyong Hyeon