Density estimation with Genetic Programming for Inverse Problem solving
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @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