Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

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  title =        "Using Genetic Programming with Prior Formula Knowledge
                 to Solve Symbolic Regression Problem",
  author =       "Qiang Lu and Jun Ren and Zhiguang Wang",
  journal =      "Computational Intelligence and Neuroscience",
  year =         "2016",
  pages =        "Article ID 1021378",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Hindawi Publishing Corporation",
  bibsource =    "OAI-PMH server at",
  identifier =   "/pmc/articles/PMC4706865/",
  language =     "en",
  oai =          "",
  rights =       "Copyright 2016 Qiang Lu et al.; This is an open access
                 article distributed under the Creative Commons
                 Attribution License, which permits unrestricted use,
                 distribution, and reproduction in any medium, provided
                 the original work is properly cited.",
  URL =          "",
  URL =          "",
  size =         "18 pages",
  abstract =     "A researcher can infer mathematical expressions of
                 functions quickly by using his professional knowledge
                 (called Prior Knowledge). But the results he finds may
                 be biased and restricted to his research field due to
                 limitation of his knowledge. In contrast, Genetic
                 Programming method can discover fitted mathematical
                 expressions from the huge search space through running
                 evolutionary algorithms. And its results can be
                 generalised to accommodate different fields of
                 knowledge. However, since GP has to search a huge
                 space, its speed of finding the results is rather slow.
                 Therefore, in this paper, a framework of connection
                 between Prior Formula Knowledge and GP (PFK-GP) is
                 proposed to reduce the space of GP searching. The PFK
                 is built based on the Deep Belief Network (DBN) which
                 can identify candidate formulas that are consistent
                 with the features of experimental data. By using these
                 candidate formulas as the seed of a randomly generated
                 population, PFK-GP finds the right formulas quickly by
                 exploring the search space of data features. We have
                 compared PFK-GP with Pareto GP on regression of eight
                 benchmark problems. The experimental results confirm
                 that the PFK-GP can reduce the search space and obtain
                 the significant improvement in the quality of SR.",

Genetic Programming entries for Qiang Lu Jun Ren Zhiguang Wang