Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Lixiong Xu and Yuan Huang and Xiaodong Shen and 
                 Yang Liu2",
  title =        "Parallelizing Gene Expression Programming Algorithm in
                 Enabling Large-Scale Classification",
  journal =      "Scientific Programming",
  year =         "2017",
  volume =       "2017",
  pages =        "5081526:1--5081526:10",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  bibdate =      "2017-05-28",
  bibsource =    "DBLP,
  URL =          "",
  DOI =          "doi:10.1155/2017/5081526",
  abstract =     "As one of the most effective function mining
                 algorithms, Gene Expression Programming (GEP) algorithm
                 has been widely used in classification, pattern
                 recognition, prediction, and other research fields.
                 Based on the self-evolution, GEP is able to mine an
                 optimal function for dealing with further complicated
                 tasks. However, in big data researches, GEP encounters
                 low efficiency issue due to its long time mining
                 processes. To improve the efficiency of GEP in big data
                 researches especially for processing large-scale
                 classification tasks, this paper presents a
                 parallelized GEP algorithm using MapReduce computing
                 model. The experimental results show that the presented
                 algorithm is scalable and efficient for processing
                 large-scale classification tasks.",
  notes =        "The Iris Dataset, The Wine Dataset",

Genetic Programming entries for Lixiong Xu Yuan Huang Xiaodong Shen Yang Liu2