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  • Differential evolution is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is suitable for a parallel implementation on multicore systems and on Graphic Processing Units (GPUs) that nowadays reach peak performance of hundreds and thousands of giga FLOPS (floating-point operations per second). This study provides a brief survey of current state-of-the-art research on differential evolution in the GPU environment.
  • Differential evolution is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is suitable for a parallel implementation on multicore systems and on Graphic Processing Units (GPUs) that nowadays reach peak performance of hundreds and thousands of giga FLOPS (floating-point operations per second). This study provides a brief survey of current state-of-the-art research on differential evolution in the GPU environment. (en)
Title
  • A brief survey of differential evolution on Graphic Processing Units
  • A brief survey of differential evolution on Graphic Processing Units (en)
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  • A brief survey of differential evolution on Graphic Processing Units
  • A brief survey of differential evolution on Graphic Processing Units (en)
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  • RIV/61989100:27240/13:86088869!RIV14-MSM-27240___
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  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
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  • 58444
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  • RIV/61989100:27240/13:86088869
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  • survey; research; general purpose GPUs; differential evolution; applications (en)
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  • [A7E7E8BF5FB7]
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  • Singapur
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  • New York
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  • Proceedings of the 2013 IEEE Symposium on Differential Evolution, SDE 2013 - 2013 IEEE Symposium Series on Computational Intelligence
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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number of pages
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  • 10.1109/SDE.2013.6601456
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  • IEEE
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  • 978-1-4673-5873-6
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  • 27240
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