Dynamic visualization design of visual symbols using interactive genetic algorithm

The dynamic visualization design of visual symbols is a complex task that requires finding a solution that meets user needs and has good visual effects. However, considering both the personalized needs of users and the dynamic visualization effect of design elements is a huge challenge. To address this challenge, this study proposes a dynamic visual symbol design method based on interactive genetic algorithm, which integrates real-time user participation mechanism and dynamic interactive feedback system to achieve intelligent and personalized design process. Compared with traditional genetic algorithms, this method introduces a dynamic fitness evaluation mechanism with direct user participation on the basis of traditional genetic algorithms. Users can evaluate the visual effects of individual populations in real time through a graphical interactive interface, and support real-time adjustment of parameters such as symbol form and motion trajectory. At the same time, it combines Bayesian probability models and Gaussian process proxy models to achieve dynamic capture and prediction of user preferences. The results show that the proposed method has a prediction accuracy of 89.1% and user satisfaction of 97.4% in dynamic visual symbol design, which is significantly improved compared to traditional genetic algorithms. This study provides new ideas for solving the dynamic design problem of multi-objective and high-dimensional visual symbols, which will help promote the development of the field of visual communication.