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 to develop a "creative" evolutionary system. Creativity in this context refers to the ability of such a system to generate solutions whose performance improves over successive generations as their structural topology evolves.

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

Cellular Automata are systems composed of cells whose states depend on their own previous states and those of neighboring cells. These systems produce complex behavior through simple rules governing local interactions between components.

Agents can be thought of as basic computational units whose behavior is governed by rules for interacting with the environment and other agents.