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  • The recent time has seen the rise of consumer grade massively parallel environments. Powerful GPUs and multi-core processors became widely available and easy to use programming APIs such as nVidia CUDA, OpenCL, and DirectCompute simplify the development of applications that can utilize them. In this environment, the nature inspired meta-heuristics can be in suitable cases implemented in parallel without additional costs. Backed by the power of modern GPGPUs, the meta-heuristics can be deployed to solve practical real world problems. In this paper, we compare differential evolution and genetic algorithms implemented on CUDA when solving the independent tasks scheduling problem.
  • The recent time has seen the rise of consumer grade massively parallel environments. Powerful GPUs and multi-core processors became widely available and easy to use programming APIs such as nVidia CUDA, OpenCL, and DirectCompute simplify the development of applications that can utilize them. In this environment, the nature inspired meta-heuristics can be in suitable cases implemented in parallel without additional costs. Backed by the power of modern GPGPUs, the meta-heuristics can be deployed to solve practical real world problems. In this paper, we compare differential evolution and genetic algorithms implemented on CUDA when solving the independent tasks scheduling problem. (en)
Title
  • A comparison of many-threaded differential evolution and genetic algorithms on CUDA
  • A comparison of many-threaded differential evolution and genetic algorithms on CUDA (en)
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  • A comparison of many-threaded differential evolution and genetic algorithms on CUDA
  • A comparison of many-threaded differential evolution and genetic algorithms on CUDA (en)
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  • RIV/61989100:27240/11:86084959!RIV13-MPO-27240___
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  • P(FR-TI1/420)
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  • 183694
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  • RIV/61989100:27240/11:86084959
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  • independent task scheduling; genetic algorithms; differential evolution; CUDA (en)
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  • [6364832AA962]
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  • Salamanca
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  • Piscataway
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  • Proceedings of the 2011 Third World Congress on Nature and Biologically Inspired Computing
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  • Abraham Padath, Ajith
  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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number of pages
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  • 10.1109/NaBIC.2011.6089641
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  • IEEE
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  • 978-1-4577-1122-0
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  • 27240
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