A new hybrid method of Evolutionary-Numerical algorithms to solve ODEs arising in physics and engineering
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{khaleghi:2023:GPEM,
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author = "S. R. Mirshafaei and H. Saberi Najafi and
E. khaleghi and A. H. Refahi Sheikhani",
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title = "A new hybrid method of Evolutionary-Numerical
algorithms to solve {ODE}s arising in physics and
engineering",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2023",
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volume = "24",
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number = "1",
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pages = "Article no. 1",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Artificial
intelligence, AI, Evolutionary algorithms, Linear and
nonlinear ODEs",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/c5KSt",
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DOI = "doi:10.1007/s10710-023-09450-6",
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size = "23 pages",
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abstract = "we aimed to use artificial intelligence to obtain a
mathematical model to approximate the exact solution
for linear and nonlinear ordinary differential
equations with initial conditions arising in physics
and engineering. To this end, genetic programming has
been implemented, along with its combination with the
Runge-Kutta fourth order method (RK4). Regarding
formulation, the produced mathematical models by this
new hybrid method (GPN) are flexible (in terms of
functions used in the model structure and the number of
them) and have acceptable accuracy compared to other
existing traditional powerful methods now in use.
Numerical experiments have been adequately conducted to
indicate the sufficient accuracy and productive power
of GPN to generate human-competitive results.",
-
notes = "Order of authors changed.
34-04.68T20
Department of Applied Mathematics, Lahijan Branch,
Islamic Azad University, Lahijan, Iran",
- }
Genetic Programming entries for
Seyed Reza Mirshafaei
Hashem Saberi Najafi
Esmaeel Khaleghi‬
Amir hosein Refahi Sheikhani
Citations