Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity

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@InProceedings{Li6:2008:cec,
  author =       "Kangshun Li and Weifeng Pan and Wensheng Zhang and 
                 Zhangxin Chen",
  title =        "Automatic Modeling of a Novel Gene Expression
                 Programming Based on Statistical Analysis and Critical
                 Velocity",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "169--173",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0167.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630794",
  abstract =     "The basic principle of GEP is briefly introduced. And
                 considering the defects of classic GEP such as lack of
                 variety, the problem of convergence and blind searching
                 without learning mechanism, a novel GEP based on
                 statistical analysis and stagnancy velocity is proposed
                 (called AMACGEP). It mainly has the following
                 characteristics: First, improve the initial population
                 by statistic analysis of repeated bodies. Second,
                 introduce the concept of stagnancy velocity to adjust
                 the searching space, evolution velocity, the diversity
                 of individuals and the accuracy of prediction. Third,
                 introduce dynamic mutation operator to improve the
                 diversity of individuals and the velocity of
                 convergence. Compared with other methods like
                 traditional methods, methods of neural network, classic
                 GEP and other improved GEPs in automatic modelling of
                 complex function, the simulation results show that the
                 AMACGEP set up by this paper is better.",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for Kangshun Li Weifeng Pan Wensheng Zhang Zhangxin (John) Chen

Citations