With the integration of technology and art, algorithmic simulation has become a core driving force in generative art, blurring the boundaries between code, nature, and artistic creation. This work aims to explore how three key algorithmic techniques--fractals, particle systems, and cellular automata--simulate natural phenomena and shape generative art, while establishing evaluation criteria for such simulations: morphological fidelity, dynamic fidelity, situated coupling, and affective remainder. Through analyzing typical works by artists such as Ryoji Ikeda, Hamid Naderi Yeganeh, Refik Anadol, and John Gerrard, the study finds that these algorithmic strategies effectively capture the structural logic and dynamic characteristics of nature, generating artworks with emergent complexity. However, digital simulations inevitably lack some sensory qualities of nature, and their ethical and ecological implications depend on situated coupling with real-world contexts and data. This work enriches the theoretical framework for evaluating generative art, highlights the unique value of algorithmic simulation in revealing natural order, and provides insights into how art can inspire ecological awareness in the digital age, bridging the gap between human, technology, and nature.
Research Article
Open Access