A fixed functional set genetic algorithm (FFSGA) approach for function approximation

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@Article{Tufail:2006:JH,
  author =       "Mohammad Tufail and Lindell E. Ormsbee",
  title =        "A fixed functional set genetic algorithm (FFSGA)
                 approach for function approximation",
  journal =      "Journal of Hydroinformatics",
  year =         "2006",
  volume =       "8",
  number =       "3",
  pages =        "193--206",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks, friction factor, functional
                 approximation, turbulent pipe flow",
  ISSN =         "1464-7141",
  URL =          "http://www.iwaponline.com/jh/008/0193/0080193.pdf",
  size =         "14 pages",
  abstract =     "This paper describes a simple mathematical technique
                 that uses a genetic algorithm and least squares
                 optimisation to obtain a functional approximation (or
                 computer program) for a given data set. Such an optimal
                 functional form is derived from a pre-defined general
                 functional formulation by selecting optimal
                 coefficients, decision variable functions, and
                 mathematical operators. In the past, functional
                 approximations have routinely been obtained through the
                 use of linear and non-linear regression analysis. More
                 recent methods include the use of genetic algorithms
                 and genetic programming. An example application based
                 on a data set extracted from the commonly used Moody
                 diagram has been used to demonstrate the utility of the
                 proposed method. The purpose of the application was to
                 determine an explicit expression for friction factor
                 and to compare its performance to other available
                 techniques. The example application results in the
                 development of closed form expressions that can be used
                 for evaluating the friction factor for turbulent pipe
                 flow. These expressions compete well in accuracy with
                 other known methods, validating the promise of the
                 proposed method in identifying useful functions for
                 physical processes in a very effective manner. The
                 proposed method is simple to implement and has the
                 ability to generate simple and compact explicit
                 expressions for a given response function.",
}

Genetic Programming entries for Mohammad Tufail Lindell Ormsbee

Citations