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Efficient hierarchical parallel genetic algorithms using grid computing

Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, Bu-Sung Lee
School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Future Generation Computer Systems, Volume 23, Issue 4, May 2007, Pages 658-670

@article{lim2007efficient,

   title={Efficient hierarchical parallel genetic algorithms using grid computing},

   author={Lim, D. and Ong, Y.S. and Jin, Y. and Sendhoff, B. and Lee, B.S.},

   journal={Future Generation Computer Systems},

   volume={23},

   number={4},

   pages={658–670},

   issn={0167-739X},

   year={2007},

   publisher={Elsevier}

}

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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.
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