A massively parallel architecture for distributed genetic algorithms

Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. In this article we describe parallel models for genetic algorithms in general and the massively...

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Bibliographic Details
Published in:Parallel computing Vol. 30; no. 5; pp. 647 - 676
Main Author: Eklund, Sven E.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2004
Subjects:
Classification
Diffusion model
FPGA
Genetic programming
Parallel architecture
Regression
Time series forecasting
FPGA
Genetic programming
Classification
Time series forecasting
Regression
Parallel architecture
Diffusion model
ISSN:0167-8191, 1872-7336
Online Access:Get full text
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Summary:Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. In this article we describe parallel models for genetic algorithms in general and the massively parallel Diffusion Model in particular, in order to speedup the execution. Implemented in hardware, the Diffusion Model constitutes an efficient, flexible, scalable and mobile machine learning system. This fine-grained system consists of a large number of processing nodes that evolve a large number of small, overlapping subpopulations. Every processing node has an embedded CPU that executes a linear machine code representation at a rate of up to 20,000 generations per second. Besides being efficient, implemented in hardware this model is highly portable and applicable to mobile, on-line applications. The architecture is also scalable so that larger problems can be addressed with a system with more processing nodes. Finally, the use of linear machine code as genetic programming representation and VHDL as hardware description language, makes the system highly flexible and easy to adapt to different applications. Through a series of experiments we determine the settings of the most important parameters of the Diffusion Model. We also demonstrate the effectiveness and flexibility of the architecture on a set of regression problems, a classification application and a time series forecasting application.
ISSN:0167-8191
1872-7336
DOI:10.1016/j.parco.2003.12.009