A genetic programming based learning system to derive multipole and local expansions for the fast multipole method

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@Article{Razavi:2012:AIc,
  title =        "A genetic programming based learning system to derive
                 multipole and local expansions for the fast multipole
                 method",
  author =       "Seyed Naser Razavi and Nicolas Gaud and 
                 Abderrafiaa Koukam and Nasser Mozayani",
  journal =      "AI Communications",
  year =         "2012",
  volume =       "25",
  month =        oct,
  number =       "4",
  pages =        "305--319",
  keywords =     "genetic algorithms, genetic programming, fast
                 multipole method, local expansion, multipole
                 expansion",
  publisher =    "IOS Press",
  ISSN =         "0921-7126",
  URL =          "http://iospress.metapress.com/content/964105681v528t63/fulltext.pdf",
  DOI =          "doi:10.3233/AIC-2012-0538",
  size =         "15 pages",
  abstract =     "This paper introduces an automatic learning algorithm
                 based on genetic programming to derive local and
                 multipole expansions required by the Fast Multipole
                 Method (FMM). FMM is a well-known approximation method
                 widely used in the field of computational physics,
                 which was first developed to approximately evaluate the
                 product of particular N by N dense matrices with a
                 vector in O(N log(N)) operations, while direct
                 multiplication requires O(N2) operations. Soon after
                 its invention, the FMM algorithm was applied
                 successfully in many scientific fields such as
                 simulation of physical systems (Electromagnetic,
                 Stellar clusters, Turbulence), Computer Graphics and
                 Vision (Light scattering) and Molecular dynamics.
                 However, FMM relies on the analytical expansions of the
                 underlying kernel function defining the interactions
                 between particles, which are not obvious to derive.
                 This is a major factor that severely limits the
                 application of the FMM to many interesting problems.
                 Thus, the proposed automatic technique in this article
                 can be regarded as a very useful tool helping
                 practitioners to apply FMM to their own problems. Here,
                 we have implemented a prototype system and tested it on
                 various types of kernels. The preliminary results are
                 very promising, and so we hope that the proposed method
                 can be applied successfully to other problems in
                 different application domains.",
  notes =        "also known as
                 \cite{RazaviGaudKoukamMozayani2012_450}",
}

Genetic Programming entries for Seyed Naser Razavi Nicolas Gaud Abderrafiaa Koukam Nasser Mozayani

Citations