Evolving parametric models using genetic programming with artificial selection

Evolutionary methods with artificial selection have been shown to be a useful computer-human technique for exploring wide design spaces with unknown goals. This paper investigates a similar approach in the evolution of visual programs currently used in popular parametric modelling software. Although associative models provide a useful cognitive artifact for the designer to interact with, they are often bound by their topological structure with the designer left to adjusting (or optimising) variables to explore the design space. By allowing the topological structure of the graph to be evolved as well as the parameters, artificial selection can be employed to explore a wide design space more suited to the early design stage