Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming

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@InProceedings{Bautu:2005:SYNASC,
  author =       "Elena Bautu and Andrei Bautu and Henri Luchian",
  title =        "Symbolic Regression on Noisy Data with Genetic and
                 Gene Expression Programming",
  booktitle =    "Seventh International Symposium on Symbolic and
                 Numeric Algorithms for Scientific Computing
                 (SYNASC'05)",
  year =         "2005",
  pages =        "321--324",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  DOI =          "doi:10.1109/SYNASC.2005.70",
  abstract =     "regression on a finite sample of noisy data. The
                 purpose is to obtain a mathematical model for data
                 which is both reliable and valid, yet the analytical
                 expression is not restricted to any particular form. To
                 obtain a statistical model of the noisy data set we use
                 symbolic regression with pseudo-random number
                 generators. We begin by describing symbolic regression
                 and our implementation of this technique using genetic
                 programming (GP) and gene expression programming (GEP).
                 We present some results for symbolic regression on
                 computer generated and real financial data sets in the
                 final part of this paper.",
}

Genetic Programming entries for Elena Bautu Andrei Bautu Henri Luchian

Citations