A program searching for a functional dependence using genetic programming with coefficient adjustment

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@InProceedings{Hlavac:2016:SCSP,
  author =       "Vladimir Hlavac",
  booktitle =    "2016 Smart Cities Symposium Prague (SCSP)",
  title =        "A program searching for a functional dependence using
                 genetic programming with coefficient adjustment",
  year =         "2016",
  abstract =     "When modelling many traffic problems, it is necessary
                 to find the functional dependence of the output of two
                 input variables. This task can be solved by a neural
                 network, by using some spline interpolation or
                 polynomials, etc. These approaches can produce a model,
                 but its internal description is unreadable and its
                 transfer to another program can be difficult.
                 Therefore, a program to determine this functional
                 dependence using genetic programming has been
                 developed. The result is prepared in such a way that it
                 can be transferred into a source code of another
                 program, or copied to an MS Excel sheet. The program
                 reads data available as triplets, [[x, y], z], and
                 looks for their functional interdependencies by using a
                 selected set of elementary functions and a vector of
                 multiplicative constants. The input data do not have to
                 meet any additional conditions. They can be defined on
                 measured intervals, or even as individual points. For a
                 successful outcome, the only condition is to have a
                 sufficient amount of data. For some functions, the
                 level of noise has to be determined in order to make
                 the model complete. In this case, noise characteristics
                 can be evaluated from the results of the program.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SCSP.2016.7501014",
  month =        may,
  notes =        "Also known as \cite{7501014}",
}

Genetic Programming entries for Vladimir Hlavac

Citations