Genetic programming of the stochastic interpolation framework: convection-diffusion equation

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@Article{Howard:2011:SC,
  title =        "Genetic programming of the stochastic interpolation
                 framework: convection-diffusion equation",
  author =       "Daniel Howard and Adrian Brezulianu and 
                 Joseph Kolibal",
  journal =      "Soft Computing",
  year =         "2011",
  number =       "1",
  volume =       "15",
  pages =        "71--78",
  bibdate =      "2011-02-19",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/soco/soco15.html#HowardBK11",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1432-7643",
  URL =          "http://link.springer.com/article/10.1007%2Fs00500-009-0520-3",
  doi =          "doi:10.1007/s00500-009-0520-3",
  size =         "8 pages",
  abstract =     "The stochastic interpolation (SI) framework of
                 function recovery from input data comprises a
                 de-convolution step followed by a convolution step with
                 row stochastic matrices generated by a mollifier, such
                 as a probability density function. The choice of a
                 mollifier and of how it gets weighted, offers
                 unprecedented flexibility to vary both the
                 interpolation character and the extent of influence of
                 neighbouring data values. In this respect, a soft
                 computing method such as a genetic algorithm or
                 heuristic method may assist applications that model
                 complex or unknown relationships between data by tuning
                 the parameters, functional and component choices
                 inherent in SI. Alternatively or additionally, the
                 input data itself can be reverse engineered to recover
                 a function that satisfies properties, as illustrated in
                 this paper with a genetic programming scheme that
                 enables SI to recover the analytical solution to a
                 two-point boundary value convection-diffusion
                 differential equation. If further developed, this
                 nascent solution method could serve as an alternative
                 to the weighted residual methods, as these are known to
                 have inherent mathematical difficulties.",
  affiliation =  "Howard Science Limited, 24 Sunrise, Malvern, WR142NJ
                 UK",
}

Genetic Programming entries for Daniel Howard Adrian Brezulianu Joseph Kolibal

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