Parallel genetic programming for decision tree induction

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

  author =       "Gianluigi Folino and Clara Pizzuti and 
                 Giandomenico Spezzano",
  title =        "Parallel genetic programming for decision tree
  booktitle =    "Proceedings of the 13th International Conference on
                 Tools with Artificial Intelligence",
  year =         "2001",
  pages =        "129--135",
  address =      "Dallas, TX USA",
  month =        "7-9 " # nov,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, decision
                 trees, genetic algorithms, learning (artificial
                 intelligence), parallel programming, J-measure, UCI
                 machine learning repository, data sets, decision tree
                 induction, fitness function, grid model, parallel
                 genetic programming, scalability",
  URL =          "",
  size =         "7 pages",
  abstract =     "A parallel genetic programming approach to induce
                 decision trees in large data sets is presented. A
                 population of trees is evolved by employing the genetic
                 operators and every individual is evaluated by using a
                 fitness function based on the J-measure. The method is
                 able to deal with large data sets since it uses a
                 parallel implementation of genetic programming through
                 the grid model and an out of core technique for those
                 data sets that do not fit in main memory. Preliminary
                 experiments on data sets from the UCI machine learning
                 repository give good classification outcomes and assess
                 the scalability of the method",
  notes =        "Inspec Accession Number: 7139478",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano