A high-efficient multi-deme genetic algorithm with better load-balance

IF 0.5 Q4 ENGINEERING, MULTIDISCIPLINARY
Wang Jie, Yuan Jiangjun
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引用次数: 1

Abstract

Genetic algorithm is a very powerful search algorithm that fits for many complex situations. However, it is very time consuming, which limits its usage. Previous work which makes use of multi-core systems to parallelise it performs well and gains much attention. This paper introduces that the load-imbalance problem in parallel genetic algorithm will incur large overhead and will limit the performance. We propose two efficient mechanisms (postponed waiting and work stealing) to achieve fine-grained schedule to solve the problem. Compared with traditional multi-deme parallel genetic algorithm, our high-efficient multi-deme genetic algorithm (HMGA) can achieve an average speedup of 1.36.
一种具有较好负载平衡性的高效多deme遗传算法
遗传算法是一种非常强大的搜索算法,适用于许多复杂的情况。然而,它非常耗时,这限制了它的使用。以往利用多核系统对其进行并行化处理的工作表现良好,受到了广泛关注。本文介绍了并行遗传算法中的负载不平衡问题,该问题会产生较大的开销并限制算法的性能。我们提出了两种有效的机制(延迟等待和工作窃取)来实现细粒度调度来解决问题。与传统的多deme并行遗传算法相比,我们的高效多deme遗传算法(HMGA)可以实现1.36的平均加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
37
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