Exploring the Evolution of Meta Parametric Models
Sam Conrad Joyce, Nazim Ibrahim · ACADIA quarterly · 2017
Parametric associative logic can describe complex design scenarios but are typically non-trivial and time consuming to develop. Optimization is being widely applied in many fields to find high-performing solutions to objective design needs, and this is being extended further to include user input to satisfy subjective preferences. However, whilst conventional optimization approaches can set good parameters for a model, they cannot currently improve the underlying logic defined by the associative topology of the model, leaving it limited to predefined domain of designs. This work looks at the application of Cartesian Genetic Programming (CGP) as a method for allowing the automatic generation, combination and modification of valid parametric models, including topology. This has value as it allows for a much greater range of solutions, and potentially computational "creativity," as it can develop unique and surprising solutions. However, the application of a genome-based definition and evolutionary optimization, respectively, to describe parametric models and develop better models for a problem, introduce many unknowns into the model generation process. This paper explains CGP as applied to parametric design and investigates the difference between using mating, mutating and both strategies together as a way of combining aspects of parent models, under selection by a genetic algorithm under random, objective and user (Interactive GA) preferences. We look into how this effects the resultant overiterated interaction in relation to both the geometry and the parametric model.