An improved Gene Expression Programming approach for symbolic regression problems

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@Article{journals/ijon/PengYQHS14,
  author =       "Yu-zhong Peng and Chang-an Yuan and Xiao Qin and 
                 JiangTao Huang and YaBing Shi",
  title =        "An improved Gene Expression Programming approach for
                 symbolic regression problems",
  journal =      "Neurocomputing",
  year =         "2014",
  volume =       "137",
  pages =        "293--301",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Evolutionary algorithm,
                 Symbolic regression, Data modeling",
  bibdate =      "2014-05-20",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijon/ijon137.html#PengYQHS14",
  URL =          "http://dx.doi.org/10.1016/j.neucom.2013.05.062",
  abstract =     "Gene Expression Programming (GEP) is a powerful
                 evolutionary method for knowledge discovery and model
                 learning. Based on the basic GEP algorithm, this paper
                 proposes an improved algorithm named S_GEP, which is
                 especially suitable for dealing with symbolic
                 regression problems. The major advantages for this
                 S_GEP method include: (1) A new method for evaluating
                 individual without expression tree; (2) a corresponding
                 expression tree construction schema for the new
                 evaluating individual method if required by some
                 special complex problems; and (3) a new approach for
                 manipulating numeric constants so as to improve the
                 convergence. A thorough comparative study between our
                 proposed S_GEP method with the primitive GEP, as well
                 as other methods are included in this paper. The
                 comparative results show that the proposed S_GEP method
                 can significantly improve the GEP performance. Several
                 well-studied benchmark test cases and real-world test
                 cases demonstrate the efficiency and capability of our
                 proposed S_GEP for symbolic regression problems.",
}

Genetic Programming entries for Yu-zhong Peng Chang-an Yuan Xiao Qin Jiangtao Huang YaBing Shi

Citations