A Method for Generating Fractal Art Patterns Based on Genetic Algorithm

The existing fractal art pattern generation methods often rely on fixed algorithms and parameter settings, which limits the quality and efficiency of pattern generation. This study utilizes the natural selection and genetic mechanisms of the Standard Genetic Algorithm (SGA) to continuously optimize pattern design through iterative processes, resulting in fractal art works that meet user needs. Firstly, the iterative function of the fractal pattern is represented as a binary tree structure, with each node representing an iteration step. Then, genetic algorithm operations (selection, crossover, mutation) are performed on these tree structures to generate new offspring patterns. The results show that compared with traditional genetic algorithm (GA) and differential evolution (DE) algorithm, SAG has a pattern generation time between 8-19.7 seconds, and the average structural similarity of the generated patterns reaches 0.908, which greatly improves the quality. Therefore, this study has significant implications for promoting the development of fractal art pattern generation research.