A parallel implementation of genetic programming that
achieves super-linear performance
D. Andre, и J. Koza. Proceedings of the International Conference on
Parallel and Distributed Processing Techniques and
Applications, III, стр. 1163--1174. Sunnyvale, CSREA, (9-11 August 1996)
Аннотация
This paper describes the successful parallel
implementation of genetic programming on a network of
processing nodes using the transputer architecture.
With this approach, researchers of genetic algorithms
and genetic programming can acquire computing power
that is intermediate between the power of currently
available workstations and that of supercomputers at
intermediate cost. This approach is illustrated by a
comparison of the computational effort required to
solve a benchmark problem. Because of the decoupled
character of genetic programming, our approach achieved
a nearly linear speed up from parallelization. In
addition, for the best choice of parameters tested, the
use of subpopulations delivered a super linear speed-up
in terms of the ability of the algorithm to solve the
problem. Several examples are also presented where the
parallel genetic programming system evolved solutions
that are competitive with human performance on the same
problem.
%0 Conference Paper
%1 andre:1996:parGP
%A Andre, David
%A Koza, John R.
%B Proceedings of the International Conference on
Parallel and Distributed Processing Techniques and
Applications
%C Sunnyvale
%D 1996
%E Arabnia, Hamid R.
%I CSREA
%K algorithms, genetic programming
%P 1163--1174
%T A parallel implementation of genetic programming that
achieves super-linear performance
%U http://www.genetic-programming.com/jkpdf/pdpta1996.pdf
%V III
%X This paper describes the successful parallel
implementation of genetic programming on a network of
processing nodes using the transputer architecture.
With this approach, researchers of genetic algorithms
and genetic programming can acquire computing power
that is intermediate between the power of currently
available workstations and that of supercomputers at
intermediate cost. This approach is illustrated by a
comparison of the computational effort required to
solve a benchmark problem. Because of the decoupled
character of genetic programming, our approach achieved
a nearly linear speed up from parallelization. In
addition, for the best choice of parameters tested, the
use of subpopulations delivered a super linear speed-up
in terms of the ability of the algorithm to solve the
problem. Several examples are also presented where the
parallel genetic programming system evolved solutions
that are competitive with human performance on the same
problem.
@inproceedings{andre:1996:parGP,
abstract = {This paper describes the successful parallel
implementation of genetic programming on a network of
processing nodes using the transputer architecture.
With this approach, researchers of genetic algorithms
and genetic programming can acquire computing power
that is intermediate between the power of currently
available workstations and that of supercomputers at
intermediate cost. This approach is illustrated by a
comparison of the computational effort required to
solve a benchmark problem. Because of the decoupled
character of genetic programming, our approach achieved
a nearly linear speed up from parallelization. In
addition, for the best choice of parameters tested, the
use of subpopulations delivered a super linear speed-up
in terms of the ability of the algorithm to solve the
problem. Several examples are also presented where the
parallel genetic programming system evolved solutions
that are competitive with human performance on the same
problem.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Sunnyvale},
author = {Andre, David and Koza, John R.},
biburl = {https://www.bibsonomy.org/bibtex/20e7adf553be3ace618b18ef0ed8700a4/brazovayeye},
booktitle = {Proceedings of the International Conference on
Parallel and Distributed Processing Techniques and
Applications},
editor = {Arabnia, Hamid R.},
interhash = {013ea3e3a58f64fd8a2793952eb839b7},
intrahash = {0e7adf553be3ace618b18ef0ed8700a4},
keywords = {algorithms, genetic programming},
month = {9-11 August},
notes = {Awarded Best Paper Award PDPTA'96},
pages = {1163--1174},
publisher = {CSREA},
size = {13 pages},
timestamp = {2008-06-19T17:35:43.000+0200},
title = {A parallel implementation of genetic programming that
achieves super-linear performance},
url = {http://www.genetic-programming.com/jkpdf/pdpta1996.pdf},
volume = {III},
year = 1996
}