A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order ODE models

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{cao:2000:ode2GP,
  author =       "Hong-Qing Cao and Li-Shan Kang and Tao Guo and 
                 Yu-Ping Chen and Hugo {de Garis}",
  title =        "A two-level hybrid evolutionary algorithm for modeling
                 one-dimensional dynamic systems by higher-order {ODE}
                 models",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics --
                 Part B: Cybernetics",
  year =         "2000",
  volume =       "40",
  number =       "2",
  pages =        "351--357",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, evolutionary algorithm, ODE models,
                 one-dimensional dynamic systems, ordinary differential
                 equation, two-level hybrid evolutionary modeling
                 algorithm, THEMA, crossover operator",
  ISSN =         "1083-4419",
  URL =          "http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf",
  size =         "7 pages",
  abstract =     "This paper presents a new algorithm for modeling
                 one-dimensional (1-D) dynamic systems by higher-order
                 ordinary differential equation (HODE) models instead of
                 the ARMA models as used in traditional time series
                 analysis. A two-level hybrid evolutionary modeling
                 algorithm (THEMA) is used to approach the modeling
                 problem of HODE's for dynamic systems. The main idea of
                 this modeling algorithm is to embed a genetic algorithm
                 (GA) into genetic programming (GP), where GP is
                 employed to optimize the structure of a model (the
                 upper level), while a GA is employed to optimize the
                 parameters of the model (the lower level). In the GA,
                 we use a novel crossover operator based on a nonconvex
                 linear combination of multiple parents which works
                 efficiently and quickly in parameter optimization
                 tasks. Two practical examples of time series are used
                 to demonstrate the THEMA's effectiveness and
                 advantages.",
}

Genetic Programming entries for Hong-Qing Cao Li-Shan Kang Tao Guo Yu-Ping Chen Hugo de Garis