Identifying Stochastic Nonlinear Dynamic Systems Using Multi-objective Hierarchical Fair Competition Parallel Genetic Programming

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

@Article{DBLP:journals/mvl/YuanB10,
  author =       "Xiao-lei Yuan and Yan Bai",
  title =        "Identifying Stochastic Nonlinear Dynamic Systems Using
                 Multi-objective Hierarchical Fair Competition Parallel
                 Genetic Programming",
  journal =      "Multiple-Valued Logic and Soft Computing",
  year =         "2010",
  volume =       "16",
  number =       "6",
  pages =        "643--660",
  note =         "Special Issue: New Trends on Swarm Intelligent
                 Systems",
  keywords =     "genetic algorithms, genetic programming, Nonlinear
                 dynamic system identification, Stochastic system
                 identification, NARX, NARMAX, HFC-GP, multi-objective
                 evolution",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  ISSN =         "1542-3980",
  URL =          "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC16.6abstracts/MVLSCv16n6p643-660Yuan.html",
  URL =          "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv16n6contents.html",
  abstract =     "A parallel evolutionary algorithm named hierarchical
                 fair competition genetic programming (HFC-GP) was
                 employed to identify stochastic nonlinear dynamic
                 systems. Nonlinear autoregressive with exogenous inputs
                 (NARX) and nonlinear autoregressive moving average with
                 exogenous inputs (NARMAX) polynomial models were used
                 to represent object systems. Multi-objective fitness
                 was used to restrict individual structure sizes during
                 the run. HFC-GP outperformed single-population GP and
                 traditional multi-population GP in combating premature
                 convergence. For all examples, good results were
                 achieved with simultaneous and accurate identification
                 of both structures and parameters. It can be concluded
                 that HFC-GP is very effective in combating premature
                 convergence and is superior to other exiting
                 identification methods.",
}

Genetic Programming entries for Xiao-Lei Yuan Yan Bai

Citations