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Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

Lim, D, Ong, Y-S, Jin, Y, Sendhoff, B and Lee, B-S (2007) Efficient Hierarchical Parallel Genetic Algorithms using Grid computing Future Generation Computer Systems, 23 (4). pp. 658-670.

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In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (1) an extended GridRPC API to conceal the high complexity of the Grid environment, and (2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, a theoretical analysis of the possible speed-up offered is presented. An empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing a significant speed-up to evolutionary design optimization in science and engineering.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Lim, D
Ong, Y-S
Jin, Y
Sendhoff, B
Lee, B-S
Date : 2007
DOI : 10.1016/j.future.2006.10.008
Additional Information : Copyright 2007 Elsevier. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, 23(4), 2007, DOI: 10.1016/j.future.2006.10.008
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:58
Last Modified : 31 Oct 2017 14:12

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