Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving

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

  author =       "Pierre Collet and Evelyne Lutton and 
                 Frederic Raynal and Marc Schoenauer",
  title =        "Polar IFS+Parisian Genetic Programming=Efficient IFS
                 Inverse Problem Solving",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  pages =        "339--361",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, fractals,
                 Iterated Functions System, inverse problem for IFS,
                 polar IFS",
  ISSN =         "1389-2576",
  URL =          "http://minimum.inria.fr/evo-lab/Publications/PolarIFS-GPEM-New.ps.gz",
  URL =          "http://www.lri.fr/~marc/EEAAX/papers/marc/gpem2000.ps.gz",
  DOI =          "doi:10.1023/A:1010065123132",
  URL =          "http://citeseer.ist.psu.edu/374242.html",
  abstract =     "This paper proposes a new method for treating the
                 inverse problem for Iterated Functions Systems (IFS)
                 using Genetic Programming. This method is based on two
                 original aspects. On the fractal side, a new
                 representation of the IFS functions, termed Polar
                 Iterated Functions Systems, is designed, shrinking the
                 search space to mostly contractive functions. Moreover,
                 the Polar representation gives direct access to the
                 fixed points of the functions. On the evolutionary
                 side, a new variant of GP, the {"}Parisian{"} approach
                 is presented. The paper explains its similarity to the
                 {"}Michigan{"} approach of Classifier Systems: each
                 individual of the population only represents a part of
                 the global solution. The solution to the inverse
                 problem for IFS is then built from a set of
                 individuals. A local contribution to the global fitness
                 of an IFS is carefully defined for each one of its
                 member functions and plays a major role in the fitness
                 of each individual. It is argued here that both
                 proposals result in a large improvement in the
                 algorithms. We observe a drastic cut-down on CPU-time,
                 obtaining good results with small populations in few
  notes =        "Article ID: 273811",

Genetic Programming entries for Pierre Collet Evelyne Lutton Frederic Raynal Marc Schoenauer