Scalable SPMD Algorithm of Evolutionary Computation

Yongmei Lei () and Jun Luo ()
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Yongmei Lei: Shanghai University, School of Computer Engineering and Science
Jun Luo: Shanghai University, School of Computer Engineering and Science

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 345-350 from Springer

Abstract: Abstract This paper addresses two parallelization techniques used for evolutionary computation. We study the grid enabled evolutionary computation model, and the differences between the coevolution and the space decomposition based parallel evolutionary algorithm are described in detail. We propose master-slave mode and equality mode used for SPMD program implementation. In this paper, we also discuss the advantages and drawbacks of these two parallel computing model. Through comparing the solution precision attained between parallel evolutionary algorithms, we stress the excellence of space decomposition based evolutionary algorithms. Finally, successful experiment results are given to show the better optimization efficiency achieved through the parallel evolutionary algorithms.

Keywords: Evolutionary Algorithm; Evolutionary Computation; Space Decomposition; Parallel Genetic Algorithm; Parallelization Technique (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_42

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DOI: 10.1007/3-540-27912-1_42

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