Evolving Parametric Models using Genetic Programming
with Artiﬁcial Selection
1University of The West of England
Evolutionary methods with artificial selection have been shown to be an effective human-computer technique for exploring design spaces with unknown goals. This paper by John Harding investigates an interactive evolution of visual programs currently used in popular parametric modelling software.
Although parametric models provide a useful cognitive artifact for designers to interact with, they are often bound by their topological structure with the designer left to adjusting (or optimising) metric variables as part of a design search.
By allowing the topological structure of the graph to be evolved as well as the parameters, artificial selection can be employed to explore a wider design space more suited to the early design stage.