Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Arpit Bhardwaj and Aditi Sakalle and 
                 Harshita Chouhan and Harshit Bhardwaj",
  title =        "Controlling The Problem Of Bloating Using Stepwise
                 Crossover And Double Mutation Technique",
  year =         "2011",
  journal =      "Advanced Computing : an International Journal",
  volume =       "2",
  number =       "6",
  pages =        "59--68",
  month =        nov,
  publisher =    "Academy \& Industry Research Collaboration Center
  keywords =     "genetic algorithms, genetic programming, bloat,
                 stepwise crossover, double mutation, elitism, fitness",
  ISSN =         "2229726X",
  URL =          "",
  broken =       "\&genre=article\&issn=2229726X\&date=2011\&volume=2\&issue=6\&spage=59",
  DOI =          "doi:10.5121/acij.2011.2606",
  size =         "10 pages",
  abstract =     "During the evolution of solutions using genetic
                 programming (GP) there is generally an increase in
                 average tree size without a corresponding increase in
                 fitness---a phenomenon commonly referred to as bloat.
                 The conception of bloat in Genetic Programming is a
                 well naturalised phenomenon characterised by
                 variable-length genomes gradually maturating in size
                 during evolution. 'In a very real sense, bloating makes
                 genetic programming a race against time, to find the
                 best solution possible before bloat puts an effective
                 stop to the search.' In this paper we are proposing a
                 Stepwise crossover and double mutation operation in
                 order to reduce the bloat. In this especial crossover
                 operation we are using local elitism replacement in
                 combination with depth limit and size of the trees to
                 reduce the problem of bloat substantially without
                 compromising the performance. The use of local elitism
                 in crossover and mutation increases the accuracy of the
                 operation and also reduces the problem of bloat and
                 further improves the performance. To shew our approach
                 we have designed a Multiclass Classifier using GP by
                 taking few benchmark datasets.",
  bibsource =    "OAI-PMH server at",
  oai =          "oai:doaj-articles:4e07bf6e3c343d42b02de6aed48a4d17",

Genetic Programming entries for Arpit Bhardwaj Aditi Sakalle Harshita Chouhan Harshit Bhardwaj