A fixed functional set genetic algorithm (FFSGA) approach for function approximation
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- @Article{Tufail:2006:JH,
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author = "Mohammad Tufail and Lindell E. Ormsbee",
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title = "A fixed functional set genetic algorithm (FFSGA)
approach for function approximation",
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journal = "Journal of Hydroinformatics",
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year = "2006",
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volume = "8",
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number = "3",
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pages = "193--206",
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month = jul,
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keywords = "genetic algorithms, genetic programming, artificial
neural networks, friction factor, functional
approximation, turbulent pipe flow",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/008/0193/0080193.pdf",
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DOI = "doi:10.2166/hydro.2006.021",
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size = "14 pages",
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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