Parallel Implementation of a Genetic-programming Based Tool for Symbolic Regression
Parallel Implementation of a Genetic-programming Based Tool for Symbolic Regression
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.
299-307
Salhi, A.
d16f4a4e-2678-4b7e-95e9-9113e25e20df
Glaser, H.
df88ca22-a72f-4fb6-9784-6578737d8af4
Roure, D. De
02879140-3508-4db9-a7f4-d114421375da
1998
Salhi, A.
d16f4a4e-2678-4b7e-95e9-9113e25e20df
Glaser, H.
df88ca22-a72f-4fb6-9784-6578737d8af4
Roure, D. De
02879140-3508-4db9-a7f4-d114421375da
Salhi, A., Glaser, H. and Roure, D. De
(1998)
Parallel Implementation of a Genetic-programming Based Tool for Symbolic Regression.
Information Processing Letters, 66, .
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.
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Published date: 1998
Organisations:
Web & Internet Science
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Local EPrints ID: 250544
URI: http://eprints.soton.ac.uk/id/eprint/250544
PURE UUID: ca828444-03de-49a6-9eef-9b7366329503
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Date deposited: 10 Aug 1999
Last modified: 08 Jan 2022 05:40
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Author:
A. Salhi
Author:
H. Glaser
Author:
D. De Roure
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