Due to the rapid development of multimedia technology, users have increasingly strong demands for personalized, scene based, and emotional interactive experiences. However, traditional multimedia interaction design has always relied on the personal experience of designers, which can easily lead to deviations in the process of requirement transmission, long optimization iteration cycles, and difficulty in achieving balance between multiple design goals. This article explores the application of genetic algorithm (GA) in multimedia interactive design. A multi-objective fitness function was established through research, combined with dynamic evolutionary strategies, to form an interpretable intelligent optimization framework. In the practical verification of educational multimedia applications, the effects of traditional empirical design, single GA optimization, and improved multi-objective GA schemes were compared. The experimental results showed that the improved algorithm performed better in multiple indicators: the completion time of user tasks decreased by 25.6% compared to the control group, user satisfaction (based on SUS score) increased by 25.9%, and the degree of adaptation of key parameters also increased by 31.5%. These results demonstrate that genetic algorithms can effectively promote the intelligent development of multimedia interaction design.
Yue Yuan (Thu,) studied this question.