Improved Approach of Genetic Programming and Applications for Data Mining

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

@InProceedings{conf/icnc/ZhangC06,
  title =        "Improved Approach of Genetic Programming and
                 Applications for Data Mining",
  author =       "Yongqiang Zhang and Huashan Chen",
  booktitle =    "Advances in Natural Computation, Second International
                 Conference, {ICNC} 2006, Proceedings, Part {I}",
  publisher =    "Springer",
  year =         "2006",
  volume =       "4221",
  editor =       "Licheng Jiao and Lipo Wang and Xinbo Gao and 
                 Jing Liu and Feng Wu",
  pages =        "816--819",
  series =       "Lecture Notes in Computer Science",
  address =      "Xi'an, China",
  month =        sep # " 24-28",
  keywords =     "genetic algorithms, genetic programming, dynamic tree
                 depth, ordinary differential equation, data mining",
  bibdate =      "2006-11-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icnc/icnc2006-1.html#ZhangC06",
  ISBN =         "3-540-45901-4",
  DOI =          "doi:10.1007/11881070_108",
  abstract =     "Genetic Programming (GP for short) is applied to a
                 benchmark of the data fitting and forecasting problems.
                 However, the increasing size of the trees may block the
                 speed of problems reaching best solution and affect the
                 fitness of best solutions. In this view, this paper
                 adopts the dynamic maximum tree depth to constraining
                 the complexity of programs, which can be useful to
                 avoid the typical undesirable growth of program size.
                 For more precise data fitting and forecasting, the
                 arithmetic operator of ordinary differential equations
                 has been made use of. To testify what and how they
                 work, an example of service life data series about
                 electron parts is taken. The results indicate the
                 feasibility and availability of improved GP, which can
                 be applied successfully for data fitting and
                 forecasting problems to some extent.",
}

Genetic Programming entries for Yongqiang Zhang Huashan Chen

Citations