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- W2016241907 abstract "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." @default.
- W2016241907 created "2016-06-24" @default.
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- W2016241907 date "2010-07-01" @default.
- W2016241907 modified "2023-09-26" @default.
- W2016241907 title "PUGACE, a cellular Evolutionary Algorithm framework on GPUs" @default.
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- W2016241907 doi "https://doi.org/10.1109/cec.2010.5586286" @default.
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