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首页> 外文期刊>International journal of computer science and network security >Comparison of Parallel Distributed Metaheuristic Optimization Algorithms in Computing Reducts
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Comparison of Parallel Distributed Metaheuristic Optimization Algorithms in Computing Reducts

机译:并行分布式成群质优化算法在计算减排中的比较

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Optimization algorithms have been applied in many fields. This paper presents performance of Genetics Algorithm and Runner-Roots Algorithm in computation of Reducts. An important area of research in Data Mining is knowledge discovery. Massive amounts of data exists in the health industry and problem is to sift through it, removing redundancy and at the same time retaining enough information to base decisions upon. As the amounts of data is huge, it is required use parallel distributed optimization methods for efficient search and PC Clusters for fast computations. The results indicate the huge benefits of parallel distributed systems to be utilized in such applications.
机译:优化算法已应用于许多领域。本文呈现了遗传算法和赛道根算法在计算计算中的性能。数据挖掘中的一个重要研究领域是知识发现。卫生行业中存在大量数据,问题是筛选它,去除冗余,并同时将足够的信息保持在基本决策。随着数据量巨大,需要使用并行分布式优化方法,了解快速计算的有效搜索和PC集群。结果表明并行分布式系统在此类应用中使用的巨大益处。

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