This research investigates the application of generative rule systems, evolutionary techniques and performance evaluation tools to the conceptual design of spatial configurations.

The aim of this research is the development of a “creative” evolutionary system. Creativity in this context refers to the ability of such an evolutionary system to elaborate solutions that over generations improve performance through evolving the structural topology.

This study investigates the application of a machine learning technique called Self-Organizing Map (SOM) for the optimal allocation of architectural functions in buildings through volumetric clustering based on daylight requirements.

Cellular Automata are systems based on cellular entities whose states depends on their previous state and on the one of their neighbours. This system performs complex outcomes by implementing simple rules that affect only local relations of their components.

Agents can be thought of as basic computational units whose behavior is based on a set of rules which depend on the interaction with the environment and other agents.