The ARGOT strategy III: the BBN Butterfly multiprocessor

E. O'Neil, C. G. Shaefer
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引用次数: 1

Abstract

The ARGOT strategy combines genetic algorithms with a mechanism providing a dynamically adaptive representation to form a robust optimization tool, as previously shown in the uniprocessor environment. For implementation of ARGOT on the BBN Butterfly multiprocessor, a parallel selection algorithm and a method of incremental payoff update were developed. These lead to enhanced parallelism and reduced the amount of computation needed by any genetic algorithms, including ARGOT. Experimental results on two matrix problems are presented, one a linear system from a FEM problem, and the other a nonlinear problem not well-behaved enough for consistent conjugate gradient results.<>
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ARGOT策略III: BBN Butterfly多处理器
ARGOT策略将遗传算法与提供动态自适应表示的机制相结合,形成一个健壮的优化工具,如前面在单处理器环境中所示。为了在BBN Butterfly多处理器上实现ARGOT,提出了一种并行选择算法和增量收益更新方法。这提高了并行性,减少了任何遗传算法(包括ARGOT)所需的计算量。本文给出了两个矩阵问题的实验结果,一个是由有限元问题得到的线性系统,另一个是由于共轭梯度结果不够一致的非线性问题
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