On the Evolution of Interest Operators using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Marc Ebner",
  title =        "On the Evolution of Interest Operators using Genetic
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and 
                 Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "6--10",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf",
  URL =          "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/gpmoravec.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/158450.html",
  size =         "5 pages",
  abstract =     "Interest operators play an important role in computer
                 vision. Depending on the type of the environment some
                 features may prove to be more advantageous than others.
                 Thus detection of interesting features has to be made
                 adaptive such that the best features according to some
                 measure are extracted. We are trying to evolve such
                 feature detectors using genetic programming. In this
                 paper we describe our results where the desired
                 operator, which is a Moravec interest operator, is
                 directly specified. We show that the problem is a
                 rather difficult one. Only an approximation to the
                 Moravec operator could be evolved using several sets of
                 elementary functions. 1 Motivation Interest operators
                 play an important role in computer vision [8]. They
                 highlight points which can be found easily using simple
                 correlation methods. They can be used to calculate
                 accurate distance information and for map building
                 [23]. However no interest operator is suitable for all
                 types of environments. A mobile robot which ma...",
  notes =        "EuroGP'98LB part of \cite{Poli:1998:egplb}",

Genetic Programming entries for Marc Ebner