We report on a parallel implementation of a tool for
symbolic regression, the algorithmic mechanism of which
is based on genetic programming, and communication is
handled using MPI. The implementation relies on a
random islands model (RIM), which combines both the
conventional islands model where migration of
individuals between islands occurs periodically and
niching where no migration takes place. The system was
designed so that the algorithm is synergistic with
parallel/distributed architectures, and works to make
use of processor time and minimum use of network
bandwidth without complicating the sequential algorithm
significantly. Results on an IBM SP2 are included.
%0 Journal Article
%1 SGD98
%A Salhi, Abdel
%A Glaser, H.
%A De Roure, D.
%D 1998
%J Information Processing Letters
%K Symbolic algorithms, genetic programming, regression
%N 6
%P 299--307
%T Parallel implementation of a genetic-programming based
tool for symbolic regression
%U doi:10.1016/S0020-0190(98)00056-8
%V 66
%X We report on a parallel implementation of a tool for
symbolic regression, the algorithmic mechanism of which
is based on genetic programming, and communication is
handled using MPI. The implementation relies on a
random islands model (RIM), which combines both the
conventional islands model where migration of
individuals between islands occurs periodically and
niching where no migration takes place. The system was
designed so that the algorithm is synergistic with
parallel/distributed architectures, and works to make
use of processor time and minimum use of network
bandwidth without complicating the sequential algorithm
significantly. Results on an IBM SP2 are included.
@article{SGD98,
abstract = {We report on a parallel implementation of a tool for
symbolic regression, the algorithmic mechanism of which
is based on genetic programming, and communication is
handled using MPI. The implementation relies on a
random islands model (RIM), which combines both the
conventional islands model where migration of
individuals between islands occurs periodically and
niching where no migration takes place. The system was
designed so that the algorithm is synergistic with
parallel/distributed architectures, and works to make
use of processor time and minimum use of network
bandwidth without complicating the sequential algorithm
significantly. Results on an IBM SP2 are included.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Salhi, Abdel and Glaser, H. and {De Roure}, D.},
bibdate = {Sat Nov 7 17:56:00 MST 1998},
biburl = {https://www.bibsonomy.org/bibtex/2742f07886ed3be9bb7de9b14fa96484c/brazovayeye},
coden = {IFPLAT},
interhash = {3debde400cd72f7a58e99fc07ddbf37d},
intrahash = {742f07886ed3be9bb7de9b14fa96484c},
issn = {0020-0190},
journal = {Information Processing Letters},
keywords = {Symbolic algorithms, genetic programming, regression},
month = {30 June},
notes = {GP_SR See \cite{DSSE-TR-97-3}},
number = 6,
pages = {299--307},
timestamp = {2008-06-19T17:50:57.000+0200},
title = {Parallel implementation of a genetic-programming based
tool for symbolic regression},
url = {doi:10.1016/S0020-0190(98)00056-8},
volume = 66,
year = 1998
}