A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Dilip Ahalpara and Abhijit Sen",
  title =        "A Sniffer Technique for an Efficient Deduction of
                 Model Dynamical Equations using Genetic Programming",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "1--12",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, local search,
                 hill climbing",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_1",
  abstract =     "A novel heuristic technique that enhances the search
                 facility of the standard genetic programming (GP)
                 algorithm is presented. The method provides a dynamic
                 sniffing facility to optimise the local search in the
                 vicinity of the current best chromosomes that emerge
                 during GP iterations. Such a hybrid approach, that
                 combines the GP method with the sniffer technique, is
                 found to be very effective in the solution of inverse
                 problems where one is trying to construct model
                 dynamical equations from either finite time series data
                 or knowledge of an analytic solution function. As
                 illustrative examples, some special function ordinary
                 differential equations (ODEs) and integrable nonlinear
                 partial differential equations (PDEs) are shown to be
                 efficiently and exactly recovered from known solution
                 data. The method can also be used effectively for
                 solution of model equations (the direct problem) and as
                 a tool for generating multiple dynamical systems that
                 share the same solution space.",
  notes =        "Mathematica. Order of partial or ordinary differential
                 equation search in sequence starting with first order
                 and increasing until satisfactory match found.

                 Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and

Genetic Programming entries for Dilip P Ahalpara Abhijit Sen