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Proceedings Paper

Asynchronous parallelism in steady-state genetic algorithms
Author(s): Vahl Scott Gordon
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Paper Abstract

Genetic algorithms (GAs) are becoming increasingly popular for signal detection, often in conjunction with neural networks. The time-intensive nature of these techniques has fostered an interest in parallel implementations. Genitor is a widely used algorithm belonging to the class of steady-state GAs which are generally believed to contain little exploitable parallelism. Parallel versions have involved fundamental changes to the algorithm by introducing islands. This paper describes how Genitor can be parallelized virtually as is, with nearly linear speedup, by rearranging the order of some of the genetic operations. An analytical method is derived which can be used for determining the amount of parallelism that can be achieved. An implementation for a shared-memory machine is described, and the resulting execution is shown to support the analysis.

Paper Details

Date Published: 30 June 1994
PDF: 8 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179238
Show Author Affiliations
Vahl Scott Gordon, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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