Abstract: This study proposed a Dynamic Tabu Search (DTSAR) that incorporates a dynamic Tabu list to solve an attribute reduction problem in Rough Set Theory. The dynamic Tabu list is used to skip the aspiration criteria and promote faster running times. A number of experiments have been conducted to evaluate the performance of the proposed technique with other published metaheuristic techniques, rough set and decision tree. DTSAR shows promising results on reduct generation time. It ranges between 0.20-22.18 min. For comparisons on the performances that are based on the number of produced reduct, DTSAR is on par with other metaheuristic techniques. DTSAR outperforms some techniques on certain datasets. Quality of classification rules generated by adopting DTSAR is comparable with those generated by Rough Set and Decision Trees Methods.