A New Dynamic Population Variation in Genetic Programming

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

@Article{journals/cai/TaoLC13,
  author =       "Yanyun Tao and Minglu Li and Jian Cao",
  title =        "A New Dynamic Population Variation in Genetic
                 Programming",
  journal =      "Computing and Informatics",
  year =         "2013",
  number =       "1",
  volume =       "32",
  pages =        "63--87",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1335-9150",
  bibdate =      "2013-04-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/cai/cai32.html#TaoLC13",
  URL =          "http://www.cai.sk/ojs/index.php/cai/article/view/1467",
  URL =          "http://arxiv.org/abs/1304.3779",
  abstract =     "A dynamic population variation (DPV) in genetic
                 programming (GP) with four innovations is proposed for
                 reducing computational effort and accelerating
                 convergence during the run of GP. Firstly, we give a
                 new stagnation phase definition and the characteristic
                 measure for it. Secondly, we propose an exponential
                 pivot function (EXP) in conjunction with the new
                 stagnation phase definition. Thirdly, we propose an
                 appropriate population variation formula for EXP.
                 Finally, we introduce a scheme using an instruction
                 matrix for producing new individuals to maintain
                 diversity of the population. The efficacy of these
                 innovations in our DPV is examined using four typical
                 benchmark problems. Comparisons among the different
                 characteristic measures have been conducted for
                 regression problems and the proposed measure performed
                 best in all characteristic measures. It is demonstrated
                 that the proposed population variation scheme is
                 superior to fixed and proportionate population
                 variation schemes for sequence induction. It is proved
                 that the new DPV has the capacity to provide solutions
                 at a lower computational effort compared with
                 previously proposed population variation methods and
                 standard genetic programming in most problems.",
}

Genetic Programming entries for Yanyun Tao Minglu Li Jian Cao

Citations