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  • Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules of independent tasks.
  • Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules of independent tasks. (en)
Title
  • Independent task scheduling by artificial immune systems, differential evolution, and genetic algorithms
  • Independent task scheduling by artificial immune systems, differential evolution, and genetic algorithms (en)
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  • Independent task scheduling by artificial immune systems, differential evolution, and genetic algorithms
  • Independent task scheduling by artificial immune systems, differential evolution, and genetic algorithms (en)
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  • RIV/61989100:27240/12:86084916!RIV13-MSM-27240___
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  • P(FR-TI1/420), S
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  • 141144
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  • RIV/61989100:27240/12:86084916
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  • machine learning; independent task scheduling; genetic algorithms; differential evolution; artificial immune systems (en)
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  • [E8E7D6DEF63E]
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  • Proceedings of the 2012 4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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number of pages
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  • 10.1109/iNCoS.2012.76
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  • IEEE
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  • 978-0-7695-4808-1
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  • 27240
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