Multi-Objective Genetic Programming for object detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Thomas Liddle and Mark Johnston and Mengjie Zhang",
  title =        "Multi-Objective Genetic Programming for object
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "In object detection, the goals of successfully
                 discriminating between different kinds of objects
                 (object classification) and accurately identifying the
                 positions of all objects of interest in a large image
                 (object localisation) are potentially in conflict. We
                 propose a Multi-Objective Genetic Programming (MOGP)
                 approach to the task of providing a decision-maker with
                 a diverse set of alternative object detection programs
                 that balance between high detection rate and low
                 false-alarm rate. Experiments on two datasets, simple
                 shapes and photographs of coins, show that it is
                 difficult for a Single-Objective GP (SOGP) system
                 (which weights the multiple objectives a priori) to
                 evolve effective object detectors, but that an MOGP
                 system is able to evolve a range of effective object
                 detectors more efficiently.",
  DOI =          "doi:10.1109/CEC.2010.5586072",
  notes =        "WCCI 2010. Also known as \cite{5586072}",

Genetic Programming entries for Thomas Liddle Mark Johnston Mengjie Zhang