Parameterized versus generative representations in structural design: an empirical comparison

Any computational approach to design, including the use of evolutionary algorithms, requires the transformation of the domain-specific knowledge into a formal design representation. This is a difficult and still not completely understood process. Its critical part is the choice of a type of design representation. The paper addresses this important issue by presenting and discussing results of a large number of design experiments in which parameterized and generative representations were used. Particularly, their computational and design related advantages and disadvantages were investigated and compared.Evolutionary design experiments reported in this paper considered two classes of structural design problems, including the design of a wind bracing system and the design of an entire structural system in a tall building. Parameterized and generative representations of the structural systems were introduced and their basic features discussed. The generative representations investigated in the paper were inspired by the processes of morphogenesis occurring in nature. Specifically, one-dimensional cellular automata were used to develop, or 'grow,' structural designs from the corresponding 'design embryos.'.The conducted research led to three major conclusions. First, generative representations based on cellular automata proved to scale well with the size of the considered design problems. Second, generative representations outperformed parameterized representations in minimizing weight of the structural systems in our problem domain by generating better designs and finding them faster. Finally, extensive experimental studies showed significant differences in optimal settings for evolutionary design experiments for the two representation types. The rate of mutation operator, the size of the parent population, and the type of the evolutionary algorithm were identified as the evolutionary parameters having the largest impact on the performance of evolutionary design processes in our problem domain.