User Directed Meta Parametric Design for Option Exploration

The potential of parametric associative models to explore large ranges of different designs is limited by our ability to manually create and modify them. While computation has been successfully used to generate variations by optimizing input parameters, adding or changing ‘components’ and ‘links’ of these models has typically been manual and human driven. The intellectual overhead and challenges of manually creating and maintaining complex parametric models has limited their usefulness in early stages of design exploration, where a quicker and wider design search is preferred. Recent methods called Meta Parametric Design using Cartesian Genetic Programming (CGP) specifically tailored to operate on parametric models, allows computational generation and topological modification for parametric models. This paper proposes the refinement of Meta Parametric techniques to quickly generate and manipulate models with a higher level of control than existing; enabling a more natural human centric user-directed design exploration process. Opening new possibilities for the computer to act as a co-creator: able to generate its own novel solutions, steered at a high-level by user(s) and able to develop convergent or divergent solutions over an extended interaction session, replicating in a faster way a human design assistant.