- author = "S. R. Mirshafaei and H. Saberi Najafi and E. khaleghi and A. H. Refahi Sheikhani",
- title = "A new hybrid method of Evolutionary-Numerical algorithms to solve {ODE}s arising in physics and engineering",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2023",
- volume = "24",
- number = "1",
- pages = "Article no. 1",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Artificial intelligence, Evolutionary algorithms, Linear and nonlinear ODEs",
- ISSN = "1389-2576",
-
URL = "
https://rdcu.be/c5KSt",
-
DOI = "
doi:10.1007/s10710-023-09450-6",
- size = "23 pages",
- 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.
Department of Applied Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran",
