PUGACE, a cellular Evolutionary Algorithm framework on GPUs
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}
}
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.
August 7, 2011 by hgpu
