The "fly algorithm" is a fast artificial
evolution-based technique devised for the exploration
of parameter space in pattern recognition applications.
In the application described, we evolve a population
which constitutes a particle-based three-dimensional
representation of the scene. Each individual represents
a three-dimensional point in the scene and may be
fitted with optional velocity parameters. Evolution is
controlled by a fitness function which contains all
pixel-level calculations, and uses classical
evolutionary operators (sharing, mutation, crossover).
The combined individual approach and low complexity
fitness function allow fast processing. Test results
and an application to mobile robotics are presented.
%0 Journal Article
%1 louchet:2002:ECAeng
%A Louchet, Jean
%A Guyon, Maud
%A Lesot, Marie-Jeanne
%A Boumaza, Amine
%D 2002
%J Pattern Recognition Letters
%K Artificial Computer Image Parameter Pattern algorithms, evolution, exploration genetic processing, programming, recognition, space vision,
%N 1-3
%P 335--345
%R doi:10.1016/S0167-8655(01)00129-5
%T Dynamic flies: a new pattern recognition tool applied
to stereo sequence processing
%V 23
%X The "fly algorithm" is a fast artificial
evolution-based technique devised for the exploration
of parameter space in pattern recognition applications.
In the application described, we evolve a population
which constitutes a particle-based three-dimensional
representation of the scene. Each individual represents
a three-dimensional point in the scene and may be
fitted with optional velocity parameters. Evolution is
controlled by a fitness function which contains all
pixel-level calculations, and uses classical
evolutionary operators (sharing, mutation, crossover).
The combined individual approach and low complexity
fitness function allow fast processing. Test results
and an application to mobile robotics are presented.
@article{louchet:2002:ECAeng,
abstract = {The {"}fly algorithm{"} is a fast artificial
evolution-based technique devised for the exploration
of parameter space in pattern recognition applications.
In the application described, we evolve a population
which constitutes a particle-based three-dimensional
representation of the scene. Each individual represents
a three-dimensional point in the scene and may be
fitted with optional velocity parameters. Evolution is
controlled by a fitness function which contains all
pixel-level calculations, and uses classical
evolutionary operators (sharing, mutation, crossover).
The combined individual approach and low complexity
fitness function allow fast processing. Test results
and an application to mobile robotics are presented.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Louchet, Jean and Guyon, Maud and Lesot, Marie-Jeanne and Boumaza, Amine},
biburl = {https://www.bibsonomy.org/bibtex/20c28efece4649d6146f4f33668b75d34/brazovayeye},
doi = {doi:10.1016/S0167-8655(01)00129-5},
interhash = {759ace547ffc404cb615c20d6acd08bd},
intrahash = {0c28efece4649d6146f4f33668b75d34},
journal = {Pattern Recognition Letters},
keywords = {Artificial Computer Image Parameter Pattern algorithms, evolution, exploration genetic processing, programming, recognition, space vision,},
month = {January},
notes = {Francais voir \cite{louchet:2002:ECA}},
number = {1-3},
pages = {335--345},
timestamp = {2008-06-19T17:45:53.000+0200},
title = {Dynamic flies: a new pattern recognition tool applied
to stereo sequence processing},
volume = 23,
year = 2002
}