Functional Scalability through Generative Representations: the Evolution of Table Designs

One of the main limitations for the functional scalability of automated design systems is the representation used for encoding designs. We argue that generative representations, those which are capable of reusing elements of the encoded design in the translation to the actual artifact, are better suited for automated design because reuse of building blocks captures some design dependencies and improves the ability to make large changes in design space. To support this argument we compare a generative and non-generative representation on a table design problem and find that designs evolved with the generative representation have higher fitness and a more regular structure. Additionally the generative representation was found to better capture the height dependency between table legs and also produced a wider range of table designs.