A two-level hybrid evolutionary algorithm for modelingone-dimensional dynamic systems by higher-order ODE models
Hong-Qing Cao
Li-Shan Kang
Tao Guo
Yu-Ping Chen
de Garis, H.
State Key Lab. of Software Eng., Wuhan Univ.;
This paper appears in: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publication Date: Apr 2000
Volume: 30,
Issue: 2
On page(s): 351-357
ISSN: 1083-4419
References Cited: 19
CODEN: ITSCFI
INSPEC Accession Number: 6578770
Digital Object Identifier: 10.1109/3477.836383
Current Version Published: 2002-08-06
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
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