集覆盖问题贪心算法的邻域扩展策略

Violeta N. Ivanova-Rohling
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引用次数: 2

摘要

我们生活在并行计算资源不断增加的时代。十年来,人们对并行数据挖掘算法进行了大量的研究。这些研究大多集中在改进现有算法的运行时间上。相比之下,我们的重点是提高解决方案的质量,或模型的准确性。我们正在寻找“智能”策略来投资并行计算资源,以便通过并行探索几种解决方案(称为“扩展”)来更好地探索搜索空间。在本文中,我们证明了不同邻域类型的基于邻域的加宽算法对集合覆盖问题贪婪算法的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neighborhood-based Strategies for Widening of the Greedy Algorithm of the Set Cover Problem
We live in the age of ever-increasing parallel computing resources. For a decade there has been intense research into parallel data mining algorithms. Most of this research is focused on improving the running time of existing algorithms. In contrast our focus is the improvement of the solution quality, or model accuracy. We are looking for "smart" strategies to invest parallel compute resources in order to achieve a better exploration of the search space by exploring several solutions in parallel, referred to as Widening. In this paper, we demonstrate the effect of neighborhood-based Widening with different types of neighborhoods for the greedy algorithm of the set cover problem.
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