Synthesizing feature agents using evolutionary computation

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

  author =       "Bir Bhanu and Yingqiang Lin",
  title =        "Synthesizing feature agents using evolutionary
  journal =      "Pattern Recognition Letters",
  year =         "2004",
  volume =       "25",
  pages =        "1519--1531",
  number =       "13",
  abstract =     "genetic programming (GP) with smart crossover and
                 smart mutation is proposed to discover integrated
                 feature agents that are evolved from combinations of
                 primitive image processing operations to extract
                 regions-of-interest (ROIs) in remotely sensed images.
                 The motivation for using genetic programming is to
                 overcome the limitations of human experts, since GP
                 attempts many unconventional ways of combination, in
                 some cases, these unconventional combinations yield
                 exceptionally good results. Smart crossover and smart
                 mutation identify and keep the effective components of
                 integrated operators called {"}agents{"} and
                 significantly improve the efficiency of GP. Our
                 experimental results show that compared to normal GP,
                 our GP algorithm with smart crossover and smart
                 mutation can find good agents more quickly during
                 training to effectively extract the regions-of-interest
                 and the learned agents can be applied to extract ROIs
                 in other similar images.",
  owner =        "wlangdon",
  URL =          "",
  month =        "1 " # oct,
  note =         "Pattern Recognition for Remote Sensing (PRRS 2002)",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.patrec.2004.06.005",
  size =         "13 pages",
  notes =        "SAR",

Genetic Programming entries for Bir Bhanu Yingqiang Lin