Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules

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

@InProceedings{Yan:2006:WCICA,
  author =       "Liping Yan and Jianchao Zeng",
  title =        "Using Particle Swarm Optimization and Genetic
                 Programming to Evolve Classification Rules",
  booktitle =    "The Sixth World Congress on Intelligent Control and
                 Automation, WCICA 2006",
  year =         "2006",
  volume =       "1",
  pages =        "3415--3419",
  address =      "Dalian",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0332-4",
  DOI =          "doi:10.1109/WCICA.2006.1713002",
  abstract =     "According to analysing particle swarm optimisation
                 (PSO), the structure of genetic programming (GP) and
                 classifier model, PSO algorithm and GP were made to
                 combine to evolve classification rules. Rules were
                 described as binary tree which non-leaf node denoted
                 rule structure and leaf-node was correspond to rule
                 value. Leaf node and non-leaf node employed different
                 evolutionary strategy. First, PSO was applied to evolve
                 leaf node in order to obtain the optimum rule of
                 certain structure, then GP was adopted to optimise rule
                 structure. The best rules were obtained after the twice
                 optimisation. Finally, the new method indicated
                 efficiency through experiments on several datasets of
                 UCI",
  notes =        "China North Univ., Taiyuan",
}

Genetic Programming entries for Liping Yan Jianchao Zeng

Citations