Mixed IFS: Resolution of the Inverse Problem Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

  author =       "Evelyne Lutton and Jacques Levy-Vehel and 
                 Guillaume Cretin and Philippe Glevarec and Cidric Roll",
  title =        "Mixed {IFS}: Resolution of the Inverse Problem Using
                 Genetic Programming",
  journal =      "Complex Systems",
  year =         "1995",
  volume =       "9",
  number =       "5",
  pages =        "375--398",
  keywords =     "genetic algorithms, genetic programming, fractals",
  URL =          "http://www.complex-systems.com/pdf/09-5-3.pdf",
  URL =          "http://www.complex-systems.com/abstracts/v09_i05_a03.html",
  size =         "24 pages",
  abstract =     "We address here the resolution of the so-called
                 inverse problem for the iterated functions system
                 (IFS). This problem has already been widely considered,
                 and some studies have been performed for the affine
                 IFS, using deterministic or stochastic methods
                 (simulated annealing or genetic algorithm). In dealing
                 with the nonaffine IFS, the usual techniques do not
                 perform well unless some a priori hypotheses on the
                 structure of the IFS (number and type of functions) are
                 made. In this work, a genetic programming method is
                 investigated to solve the ``general'' inverse problem,
                 which allows the simultaneous performance of a numeric
                 and a symbolic optimization. The use of a ``mixed IFS''
                 may enlarge the scope of some applications, for
                 example, image compression, because it allows a wider
                 range of shapes to be coded.",
  notes =        "see also \cite{Cretin:al:EA95}

Genetic Programming entries for Evelyne Lutton Jacques Levy-Vehel Guillaume Cretin Philippe Glevarec Cedric Roll