MRPGA: An Extension of MapReduce for Parallelizing Genetic Algorithms
作者： Chao Jin , Christian Vecchiola , Rajkumar Buyya , None
关键词:
摘要: The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many cannot be exactly expressed with due their specific characteristics. For instance, genetic algorithms (GAs) naturally fit into an iterative style. That does not follow the two phase pattern of MapReduce. This paper presents extension featuring a hierarchical reduction phase. is called MRPGA (MapReduce for parallel GAs), which can automatically parallelize GAs. We describe design and implementation extended on .NET-based enterprise grid system detail. evaluation this its runtime presented using example applications.
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