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Scalable SPMD Algorithm of Evolutionary Computation

In: Current Trends in High Performance Computing and Its Applications

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  • Yongmei Lei

    (Shanghai University, School of Computer Engineering and Science)

  • Jun Luo

    (Shanghai University, School of Computer Engineering and Science)

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.

Suggested Citation

  • Yongmei Lei & Jun Luo, 2005. "Scalable SPMD Algorithm of Evolutionary Computation," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 345-350, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_42
    DOI: 10.1007/3-540-27912-1_42
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