Understanding Evolved Genetic Programs for a Real World Object Detection Problem

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

  author =       "Victor Ciesielski and Andrew Innes and Sabu John and 
                 John Mamutil",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Understanding Evolved Genetic Programs for a Real
                 World Object Detection Problem",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "351--360",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "We describe an approach to understanding evolved
                 programs for a real world object detection problem,
                 that of finding orthodontic landmarks in cranio-facial
                 X-Rays. The approach involves modifying the fitness
                 function to encourage the evolution of small programs,
                 limiting the function set to a minimal number of
                 operators and limiting the number of terminals
                 (features). When this was done for two landmarks, an
                 easy one and a difficult one, the evolved programs
                 implemented a linear function of the features. Analysis
                 of these linear functions revealed that underlying
                 regularities were being captured and that successful
                 evolutionary runs usually terminated with the best
                 programs implementing one of a small number of
                 underlying algorithms. Analysis of these algorithms
                 revealed that they are a realistic solution to the
                 object detection problem, given the features and
                 operators available.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",

Genetic Programming entries for Victor Ciesielski Andrew Innes Sabu John John Mamutil