Particle Swarm Optimization Based Tuning of Genetic Programming Evolved Classifier Expressions

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "Particle Swarm Optimization Based Tuning of Genetic
                 Programming Evolved Classifier Expressions",
  booktitle =    "Nature Inspired Cooperative Strategies for
                 Optimization, NICSO 2010",
  editor =       "Juan Ram{\'o}n Gonz{\'a}lez and David A. Pelta and 
                 Carlos Cruz and Germ{\'a}n Terrazas and 
                 Natalio Krasnogor",
  series =       "Studies in Computational Intelligence",
  volume =       "284",
  year =         "2010",
  pages =        "385--397",
  address =      "Granada, Spain",
  month =        may # " 12-14",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, PSO",
  isbn13 =       "978-3-642-12537-9",
  DOI =          "doi:10.1007/978-3-642-12538-6_32",
  bibsource =    "DBLP,",
  abstract =     "Genetic Programming (GP) has recently emerged as an
                 effective technique for classifier evolution. One
                 specific type of GP classifiers is arithmetic
                 classifier expression trees. In this paper we propose a
                 novel method of tuning these arithmetic classifiers
                 using Particle Swarm Optimization (PSO) technique. A
                 set of weights are introduced into the bottom layer of
                 evolved GP classifier expression tree, associated with
                 each terminal node. These weights are initialized with
                 random values and optimized using PSO. The proposed
                 tuning method is found efficient in increasing
                 performance of GP classifiers with lesser computational
                 cost as compared to GP evolution for longer number of
                 generations. We have conducted a series of experiments
                 over datasets taken from UCI ML repository. Our
                 proposed technique has been found successful in
                 increasing the accuracy of classifiers in much lesser
                 number of function evaluations.",
  notes =        "NICSO",

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig