Convergency of Genetic Regression in Data Mining based on Gene Expression Programming and Optimized Solution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Yuan:2006:IJCA,
  author =       "Chang an Yuan and Chang jie Tang and Y. Wen and 
                 Jie Zuo and Jing Peng and Jian jun Hu",
  title =        "Convergency of Genetic Regression in Data Mining based
                 on Gene Expression Programming and Optimized Solution",
  journal =      "International Journal of Computers and Applications",
  year =         "2006",
  volume =       "28",
  number =       "4",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Data mining, genetic
                 regression, convergency in probability, minimised
                 residual sum of square genetic algorithm",
  DOI =          "doi:10.2316/Journal.202.2006.4.202-1831",
  abstract =     "This paper investigates the convergency of the
                 probability of genetic regression in data mining based
                 on Gene Expression Programming (GEP) and the proposed
                 optimised algorithm based on GEP Minimised Residual Sum
                 of Square Genetic Algorithm (MRSSGA). By extensive
                 experiments on Genetic Programming (GP), GEP and MRSSGA
                 show: (1) that all algorithms could find the target
                 function from the data with low noise; (2) by comparing
                 the convergency speeds, new algorithms in GEP are 20
                 times faster than GP and MRSSGA and 60 times faster
                 than GP for simple data; (3) for very complex data with
                 an unknown function type, GEP and MRSSGA are
                 respectively 900 and 1800 times faster than GP at
                 finding ideal functions; and (4) aimed at the actual
                 data, the precision of models created by using genetic
                 regression methods is much more exact than traditional
                 methods.",
}

Genetic Programming entries for Chang-an Yuan Changjie Tang Y Wen Jie Zuo Jing Peng Jianjun Hu

Citations