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User fatigue is the main bottleneck of interactive genetic algorithm, influencing its performance in searching and limiting its applications in complicated optimization problems. One of the efficient methodologies is to speed up the algorithm's convergence to satisfactory solutions by sufficiently using evolutionary knowledge. A grid-based knowledge-guided interactive genetic algorithm is proposed in this paper so as to alleviate user fatigue with less memory cost and higher computational efficiency. From the view of gene sense unit, two 3-dimensional irregular memory grids are built to store all evolutionary information, including the emerged individuals, their emerged frequency and fitness. Then, the emerged frequency and fitness of each gene sense unit are statistical computed along with the evolution. According to the obtained knowledge of a gene sense unit, the time that the user's preference is clear is determined and strategies for using such information to mutate and generate child population are designed. The proposed algorithm is applied to a curtain design system, and the results show its feasibility and efficiency in alleviating user fatigue.