Genetic Programming and Automatic Differentiation Algorithms Applied to the Solution of Ordinary and Partial Differential Equation

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Lobao:2016:CEC,
  author =       "Waldir J. A. Lobao and Douglas Mota Dias and 
                 Marco Aurelio C. Pacheco",
  title =        "Genetic Programming and Automatic Differentiation
                 Algorithms Applied to the Solution of Ordinary and
                 Partial Differential Equation",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "5286--5292",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7748362",
  abstract =     "This paper investigates the potential of evolutionary
                 algorithms, developed by the combination of genetic
                 programming (GP) and automatic differentiation methods
                 (AD), in determining analytic solutions to ordinary and
                 partial differential equations (ODE and PDE). In turn,
                 AD is a set of techniques based on the mechanical
                 application of the chain rule to numerically evaluate
                 the derivative of a function specified by a computer
                 program. The AD method has a fundamental role in this
                 work since it calculates the exact values of the
                 derivatives of a function for a given set of input
                 values while numerical differentiation methods
                 introduce unacceptable round-off errors in the
                 discretization process. With this purpose, and using
                 the Matlab programming environment, we developed
                 several algorithms (namely GPAD) and addressed problems
                 of different kinds of differential equations. The
                 results are promising, with exact solutions obtained
                 for most of the addressed problems, which include
                 equations where not even commercial systems could find
                 a symbolic solution. These results empirically indicate
                 that GPAD can be an efficient and robust methodology to
                 find analytic solutions for ODE and PDE.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Waldir J A Lobao Douglas Mota Dias Marco Aurelio Cavalcanti Pacheco

Citations