A survey and taxonomy of performance improvement of canonical genetic programming

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

  title =        "A survey and taxonomy of performance improvement of
                 canonical genetic programming",
  author =       "Peyman Kouchakpour and Anthony Zaknich and 
                 Thomas Braunl",
  journal =      "Knowledge and Information Systems",
  year =         "2009",
  number =       "1",
  volume =       "21",
  pages =        "1--39",
  keywords =     "genetic algorithms, genetic programming, Computational
                 effort, Efficiency, Performance improvement, Taxonomy",
  DOI =          "doi:10.1007/s10115-008-0184-9",
  bibdate =      "2009-12-14",
  bibsource =    "DBLP,
  abstract =     "The genetic programming (GP) paradigm, which applies
                 the Darwinian principle of evolution to hierarchical
                 computer programs, has been applied with breakthrough
                 success in various scientific and engineering
                 applications. However, one of the main drawbacks of GP
                 has been the often large amount of computational effort
                 required to solve complex problems. Much disparate
                 research has been conducted over the past 25 years to
                 devise innovative methods to improve the efficiency and
                 performance of GP. This paper attempts to provide a
                 comprehensive overview of this work related to
                 Canonical Genetic Programming based on parse trees and
                 originally championed by Koza (Genetic programming: on
                 the programming of computers by means of natural
                 selection. MIT, Cambridge, 1992). Existing approaches
                 that address various techniques for performance
                 improvement are identified and discussed with the aim
                 to classify them into logical categories that may
                 assist with advancing further research in this area.
                 Finally, possible future trends in this discipline and
                 some of the open areas of research are also

Genetic Programming entries for Peyman Kouchakpour Anthony Zaknich Thomas Braunl