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In this paper, we propose to improve the SvS algorithm by skipping elements of the smaller set for reducing a search boundary. The boundary of skipped element is determined by boundaries of elements prior to and next to it. We perform experiments on uniformly distributed random datasets to compare our algorithm with the standard SvS. We use regression analysis to get an equation for determining an appropriate skipping number. Our results show that the skipping SvS algorithm using the equation obtained can reduce approximately 41% on the number of comparisons of the standard SvS algorithm.