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CUDA Based Enhanced Differential Evolution: a Computational Analysis

Donald Davendra, Jan Gaura, Magdalena Bialic-Davendra, Roman Senkerik
Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic
26th European Conference on Modelling and Simulation, 2012

@article{davendra2012cuda,

   title={CUDA Based Enhanced Differential Evolution: a Computational Analysis},

   author={Davendra, Donald and Gaura, Jan and Bialic-Davendra, Magdalena and Senkerik, Roman},

   year={2012}

}

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General purpose graphic programming unit (GPGPU) programming is a novel approach for solving parallel variable independent problems. The graphic processor core (GPU) gives the possibility to use multiple blocks, each of which contains hundreds of threads. Each of these threads can be visualized as a core onto itself, and tasks can be simultaneously sent to all the threads for parallel evaluations. This research explores the advantages of applying a evolutionary algorithm (EA) on the GPU in terms of computational speedups. Enhanced Differential Evolution (EDE) is applied to the generic permutative flowshop scheduling (PFSS) problem both using the central processing unit (CPU) and the GPU, and the results in terms of execution time is compared.
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