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PUGACE, a cellular Evolutionary Algorithm framework on GPUs

Nicolas Soca, Jose Luis Blengio, Martin Pedemonte, Pablo Ezzatti
Centro de Calculo – Instituto de Computacion, Universidad de la Republica, J. H. Reissig 575, Montevideo, Uruguay
IEEE Congress on Evolutionary Computation (CEC), 2010

@inproceedings{soca2010pugace,

   title={PUGACE, a cellular evolutionary algorithm framework on GPUs},

   author={Soca, N. and Blengio, J.L. and Pedemonte, M. and Ezzatti, P.},

   booktitle={Evolutionary Computation (CEC), 2010 IEEE Congress on},

   pages={1–8},

   year={2010},

   organization={IEEE}

}

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Metaheuristics are used for solving optimization problems since they are able to compute near optimal solutions in reasonable times. However, solving large instances it may pose a challenge even for these techniques. For this reason, metaheuristics parallelization is an interesting alternative in order to decrease the execution time and to provide a different search pattern. In the last years, GPUs have evolved at a breathtaking pace. Originally, they were specific-purpose devices, but in a few years they became general-purpose shared memory multiprocessors. Nowadays, these devices are a powerful low cost platform for implementing parallel algorithms. In this paper, we present a preliminary version of PUGACE, a cellular Evolutionary Algorithm framework implemented on GPU. PUGACE was designed with the goal of providing a tool for easily developing this kind of algorithms. The experimental results when solving the Quadratic Assignment Problem are presented to show the potential of the proposed framework.
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