Master-slave parallel genetic algorithm based on MapReduce using cloud computing

Guang Ming Li*, Wen Hua Zeng, Jian Feng Zhao, Liu Min

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The 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 linearspeed-up with Mapper's number.

Original languageEnglish
Title of host publicationFrontiers of Manufacturing and Design Science II
Pages4023-4027
Number of pages5
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2nd International Conference on Frontiers of Manufacturing and Design Science, ICFMD 2011 - Taichung, Taiwan, Province of China
Duration: 11 Dec 201113 Dec 2011

Publication series

NameApplied Mechanics and Materials
Volume121-126
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2nd International Conference on Frontiers of Manufacturing and Design Science, ICFMD 2011
Country/TerritoryTaiwan, Province of China
CityTaichung
Period11/12/1113/12/11

Keywords

  • Cloud computing
  • Hadoop
  • MapReduce
  • Master-slave PGA
  • Parallelgenetic algorithms

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