ARTIFICIAL EVOLUTION OF COMPUTER GRAPHICS

Artificial Evolution is a computational approach inspired by biological evolution that is increasingly being applied in the field of computer graphics. It uses evolutionary algorithms such as genetic algorithms, neuro evolution, novelty search, and interactive evolutionary computation to automatically generate visual content. These methods enable systems to explore large and complex design spaces, producing diverse and creative graphical outputs such as images, textures, terrains, animations, and shaders. Recent research from 2021 to 2025 highlights the growing importance of artificial evolution in creative applications where traditional rule-based design is limited. Many studies have also combined evolutionary techniques with deep learning models, including Generative Adversarial Networks (GANs), to enhance realism and control in generated visuals. Additionally, interactive evolutionary systems allow users to guide the generation process based on subjective preferences, improving artistic outcomes. This paper reviews thirty research papers related to artificial evolution in computer graphics, analyzes their objectives and methodologies, and compares key contributions. The study demonstrates that artificial evolution is a powerful tool for automated creativity and innovation in modern graphics systems.