Sustainable Architecture Through Cellular Automata Geometries, Ai Integration and Evolutionary Solvers

This study explores the integration of Cellular Automata (CA) with Artificial Intelligence (AI) and Evolutionary Solvers to achieve sustainable and energy-efficient architectural designs. By harnessing CA's ability to optimize and modulate solar radiation gain, the research aims to enhance energy efficiency while preventing overheating. The methodological framework involves digital experiments utilizing Finite-State Automata, Diffusion Models, and Evolutionary Solvers, combined with neural networks and genetic algorithms. The results demonstrate CA's capability to create adaptive geometries that optimize solar radiation and reduce reliance on artificial cooling and heating. This approach highlights the potential for buildings to dynamically respond to environmental inputs, promoting sustainability and occupant comfort. The study's findings underscore the innovative pathways opened by CA, AI, and Evolutionary Solvers, setting a precedent for future research and development in sustainable architecture.