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dc.contributor.authorLi Guang Mingzh_CN
dc.contributor.authorZeng Wen Huazh_CN
dc.contributor.authorZhao Jian Fengzh_CN
dc.contributor.authorLiu Minzh_CN
dc.contributor.author曾文华zh_CN
dc.date.accessioned2015-07-22T02:16:05Z
dc.date.available2015-07-22T02:16:05Z
dc.date.issued2012zh_CN
dc.identifier.citationFRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6, 2012,121-126:4023-4027zh_CN
dc.identifier.otherWOS:000307425402132zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/85703
dc.descriptionConference Name:2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011). Conference Address: Taiwan, TAIWAN. Time:DEC 11-13, 2011.zh_CN
dc.description.abstractThe implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper's number.zh_CN
dc.language.isoen_USzh_CN
dc.publisherAPPL MECH MATERzh_CN
dc.source.urihttp://dx.doi.org/10.4028/www.scientific.net/AMM.121-126.4023zh_CN
dc.titleMaster-Slave parallel genetic algorithm based on MapReduce using cloud computingzh_CN
dc.typeConferencezh_CN


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