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:1.8612

@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

Citations