01276nas a2200205   4500000000100000000000100001008004100002260001200043653002300055653001000078653002400088100001900112700001700131245007000148856008800218300001000306490000600316520073400322022001401056       2015                            d  c03/201510aGenetic Algorithms10aGPGPU10aParallel Processing1 aÁlvaro Arranz1 aManuel Alvar00aGPGPU Implementation of a Genetic Algorithm for Stereo Refinement  uhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2015/02/ijimai20153_2_9.pdf  a69-760 v33 aDuring the last decade, the general-purpose computing on graphics processing units Graphics  (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques.   This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement.  The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.  a1989-1660