Application of advanced Grammatical Evolution to function prediction problem

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@Article{Kuroda20101287,
  author =       "Takuya Kuroda and Hiroto Iwasawa and Eisuke Kita",
  title =        "Application of advanced Grammatical Evolution to
                 function prediction problem",
  journal =      "Advances in Engineering Software",
  volume =       "41",
  number =       "12",
  pages =        "1287--1294",
  year =         "2010",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2010.09.005",
  URL =          "http://www.sciencedirect.com/science/article/B6V1P-5167DR5-1/2/d992cacdff191a5bc78722add7146d07",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, GE, Backus Naur Form, BNF, Function
                 prediction, Santa Fe trail, Nikkei stock average",
  abstract =     "Grammatical Evolution (GE) is one of the evolutionary
                 algorithms to find functions and programs, which can
                 deal according to a syntax with tree structure by
                 one-dimensional chromosome of Genetic Algorithm. An
                 original GE starts from the definition of the syntax by
                 means of Backus Naur Form (BNF). Chromosome in binary
                 number is translated to that in decimal number. The BNF
                 syntax selects according to the remainder of the
                 decimal number with respect to the total number of
                 candidate rules. In this study, we will introduce three
                 schemes for improving the convergence property of the
                 original GE. In numerical examples, the original GE is
                 compared in function identification problem with the
                 Genetic Programming (GP), which is one of the most
                 popular evolutionary algorithm to find unknown
                 functions or programs. Three algorithms are compared in
                 Santa Fe trail problem and prediction problem of Nikkei
                 stock average, which finds programs to control
                 artificial ants collecting foods. The results show that
                 the efficiency of schemes depends on the problem to be
                 solved and that the schemes 1 and 2 are effective for
                 Santa Fe trail problem and prediction problem of Nikkei
                 stock average, respectively.",
}

Genetic Programming entries for Takuya Kuroda Hiroto Iwasawa Eisuke Kita

Citations