Polynomial Modeling in a Dynamic Environment based on a Particle Swarm Optimization

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

  author =       "Kit Yan Chan and Tharam S. Dillon",
  title =        "Polynomial Modeling in a Dynamic Environment based on
                 a Particle Swarm Optimization",
  booktitle =    "Computational Intelligence and Its Applications",
  publisher =    "World Scientific",
  year =         "2012",
  editor =       "H K Lam and Steve S H Ling and Hung T Nguyen",
  pages =        "23--38",
  keywords =     "genetic algorithms, genetic programming, particle
                 swarm optimisation, PSO, time-varying modelling,
                 time-varying systems, polynomial modelling,
                 evolutionary computation",
  isbn13 =       "978-1-84816-691-2",
  DOI =          "doi:10.1142/9781848166929_0002",
  bibsource =    "OAI-PMH server at espace.library.curtin.edu.au",
  oai =          "oai:espace.library.curtin.edu.au:189166",
  URL =          "http://espace.library.curtin.edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=189166",
  abstract =     "In this chapter, a particle swarm optimisation (PSO)
                 is proposed for polynomial modelling in a dynamic
                 environment. The basic operations of the proposed PSO
                 are identical to the ones of the original PSO except
                 that elements of particles represent arithmetic
                 operations and polynomial variables of polynomial
                 models. The performance of the proposed PSO is
                 evaluated by polynomial modelling based on a set of
                 dynamic benchmark functions in which their optima are
                 dynamically moved. Results show that the proposed PSO
                 can find significantly better polynomial models than
                 genetic programming (GP) which is a commonly used
                 method for polynomial modelling.",

Genetic Programming entries for Kit Yan Chan Tharam S Dillon