To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Ankit Bakshi and Pallavi Pandit and Santosh Easo",
  title =        "To Accomplish Amelioration Of Classifier Using
                 Gene-Mutation Tactics In Genetic Programming",
  journal =      "International Journal of Emerging Technology and
                 Advanced Engineering",
  year =         "2012",
  volume =       "2",
  number =       "12",
  pages =        "319--322",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, elitism,
                 double tournament, plain crossover",
  ISSN =         "2250--2459",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "4 pages",
  abstract =     "A phenomenon for designing classifier for three or
                 more classes (Multiclass) problem using genetic
                 programming (GP) is multiclass classifier. In this
                 scenario we purported three methods named Double
                 Tournament Method, Gene-Mutation Method and a Plain
                 Crossover method. In Double Tournament Method, we pick
                 out two idiosyncratic for the crossover operation on
                 the basis of size and fitness. In Gene-Mutation tactic
                 we are propagating two child from single parent and
                 selecting one of them on the basis of fitness and also
                 bring into play elitism on the child so that the
                 mutation operation does not degrade the fitness of the
                 distinct, whereas in Plain Crossover we select the two
                 child for the succeeding generation on the basis of
                 size, depth and fitness along with elitism on each step
                 from the six child which is generated during crossover.
                 To exhibit our approach we have designed a Multiclass
                 Classifier using GP by taking some standard datasets.
                 The results attained show that by applying Plain
                 crossover together with Gene-Mutation refined the
                 performance of the classifier.",
  notes =        "Article 59.",

Genetic Programming entries for Ankit Bakshi Pallavi Pandit Santosh Easo