An Improved Gene Expression Programming Based on Niche Technology of Outbreeding Fusion

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@Article{journals/informaticaSI/WangZWZT17,
  author =       "Chao-xue Wang and Jing-jing Zhang and Shu-ling Wu and 
                 Fan Zhang and Jolanda G. Tromp",
  title =        "An Improved Gene Expression Programming Based on Niche
                 Technology of Outbreeding Fusion",
  journal =      "Informatica (Slovenia)",
  year =         "2017",
  volume =       "41",
  number =       "1",
  pages =        "25--30",
  month =        mar,
  note =         "Special issue on End-user, Privacy, Security and
                 Copyright issues",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "0350-5596",
  bibdate =      "2018-01-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/informaticaSI/informaticaSI41.html#WangZWZT17",
  URL =          "http://www.informatica.si/index.php/informatica/article/view/1593",
  URL =          "http://www.informatica.si/index.php/informatica/article/view/1593/960",
  size =         "6 pages",
  abstract =     "An improved Gene Expression Programming (GEP) based on
                 niche technology of outbreeding fusion (OFN-GEP) is
                 proposed to overcome the insufficiency of traditional
                 GEP in this paper. The main improvements of OFN-GEP are
                 as follows: (1) using the population initialization
                 strategy of gene equilibrium to ensure that all genes
                 are evenly distributed in the coding space as far as
                 possible; (2) introducing the outbreeding fusion
                 mechanism into the niche technology, to eliminate the
                 kin individuals, fuse the distantly related
                 individuals, and promote the gene exchange between the
                 excellent individuals from niches. To validate the
                 superiority of the OFN-GEP, several improved GEP
                 proposed in the related literatures and OFN-GEP are
                 compared about function finding problems. The
                 experimental results show that OFN-GEP can effectively
                 restrain the premature convergence phenomenon, and
                 promises competitive performance not only in the
                 convergence speed but also in the quality of
                 solution.",
}

Genetic Programming entries for Chao-xue Wang Jing-jing Zhang Shu-ling Wu Fan Zhang Jolanda G Tromp

Citations