From Mondrian to Frank Lloyd Wright: Transforming Evolving Representations

Abstract

If a computer is to create designs with the goal of following a certain style it has to have information about this style. Unfortunately, the most often used method of formal representations of style, shape grammars, does not lend itself to automated implementation. However, It has been shown how an evolutionary system with evolving representation can provide an alternative approach that allows a system to learn style knowledge automatically and without the need for an explicit representation. This paper shows how the applicability of evolved representation can be extended by the introduction of transformations of the representation. One such transformation allows mixing of style knowledge, similar to the cross-breeding of animals of different races, with the added possibility of controlling exactly what features are used from which source. This can be achieved through different ways of mixing representations learned from different examples and then using the new, combined representation to create new designs. In a similar manner, information learned in one application domain can be used in a different domain. To achieve this, either the representation or the genotype-phenotype transformation has to be adapted. The same operations also allow mixing of knowledge from different domains. As an example, we show how style information learned from a set of Mondrian paintings can be combined with style information from a Frank Lloyd Wright window design, to create new window designs. Also, we show how the combined style information can then be used to create three-dimensional objects, showing style features similar to the newly designed windows.