Density estimation with Genetic Programming for Inverse Problem solving

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{eurogp07:defoin,
  author =       "Michael {Defoin Platel} and S\'ebastien Verel and 
                 Manuel Clergue and Malik Chami",
  title =        "Density estimation with Genetic Programming for
                 Inverse Problem solving",
  editor =       "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and 
                 Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
  booktitle =    "Proceedings of the 10th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4445",
  year =         "2007",
  address =      "Valencia, Spain",
  month =        "11-13 " # apr,
  pages =        "45--54",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-71602-5",
  isbn13 =       "978-3-540-71602-0",
  DOI =          "doi:10.1007/978-3-540-71605-1_5",
  abstract =     "This paper addresses the resolution, by Genetic
                 Programming (GP) methods, of ambiguous inverse
                 problems, where for a single input, many outputs can be
                 expected. We propose two approaches to tackle this kind
                 of many-to-one inversion problems, each of them based
                 on the estimation, by a team of predictors, of a
                 probability density of the expected outputs. In the
                 first one, Stochastic Realisation GP, the predictors
                 outputs are considered as the realisations of an
                 unknown random variable which distribution should
                 approach the expected one. The second one, Mixture
                 Density GP, directly models the expected distribution
                 by the mean of a Gaussian mixture model, for which
                 genetic programming has to find the parameters.
                 Encouraging results are obtained on four test problems
                 of different difficulty, exhibiting the interests of
                 such methods.",
  notes =        "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
                 conjunction with EvoCOP2007, EvoBIO2007 and
                 EvoWorkshops2007",
}

Genetic Programming entries for Michael Defoin Platel Sebastien Verel Manuel Clergue Malik Chami

Citations