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- W2135162134 abstract "This paper describes a method of estimation of ordinary differential dynamics by Genetic Programming (GP) and discusses the accuracy of estimation. The dynamics is expressed by a tree structure and we obtain a numerical solution by carrying out digital integration (Runge-Kutta-Gill method) of the function system. The square error of the numerical solution and the original data is calculated and the inverse of the error is used as an evaluation of the individuals (functions) of the GP. Genetic operations such as crossover and mutation are carried out on pairs of solution candidates with high evaluation values, and the solutions (functions) are optimized. In addition, a coevolutional model that can efficiently perform simultaneous estimation of multiple functions is proposed. In the study of the estimation error, two systems, the Lorenz and Rössler systems, which are representatives of the chaotic ordinary differential model, are estimated and it is shown that a function of the same shape as the original one can be estimated in one-function estimation, and that in multiple-function (three-function) estimation, the coevolutional model is effective. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(2): 1–12, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10057" @default.
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- W2135162134 date "2002-10-31" @default.
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- W2135162134 title "Estimation of chaotic ordinary differential equations by coevolutional genetic programming" @default.
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- W2135162134 doi "https://doi.org/10.1002/ecjc.10057" @default.
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