Design Exploration Using A Shape Grammar With A Genetic Algorithm

Abstract

Although the idea of linking a shape grammar to a genetic algorithm is not new, this paper proposes a novel way of combining these two elements in order to provide a tool that can be used for design exploration. Using a shape grammar for design generation provides a way of creating a range of potential solutions to a design problem which fit with the designer's stylistic agenda. A genetic algorithm can then be used to take these designs and develop them into a much richer set of solutions which can still be recognised as part of the same family. By setting quantifiable targets for design performance, the genetic algorithm can evolve new designs which exhibit the best features of previous generations. The designer is then presented with a wide range of high scoring solutions and can choose which of these to take forward and develop in the conventional manner. The novelty of the proposed approach is in the use of a shape code, which describes the steps that the shape grammar has taken to create each design. The genetic algorithm works on this shape code by applying crossover and mutation in order to create a range of designs that can be tested. The fittest are then selected in order to provide the genetic material for the next generation. A prototype version of such a program, called Shape Evolution, has been developed. In order to test Shape Evolution it has been used to design a range of apartment buildings which are required to meet certain performance criteria.

Regional classification according to PM10 concentrations in the Seoul Metropolitan and Chungcheong Area, Republic of Korea

“…Genetic exploration involves generating novel solutions by merging elements from preexisting designs to widen the range of references. This approach has been applied in diverse domains such as garden design [45], 3D modeling [14,70], architecture [82], and 2D graphics [88]. However, the primary aim of this approach is to enrich the reference in the information-gathering stage by utilizing existing references, rather than supporting designers to generate their own ideas from those recombinations in the next stage.…”

“…However, these approaches primarily emphasize generating precise combinations that effectively incorporate all elements harmoniously, rather than aiming to produce diverse combinations for exploratory purposes. Another thread of research focuses on searching by genetic recombination [14,45,82,88], but these techniques were focused on widening the range of the design exploration, rather than offering inspiration on how to effectively combine specific design elements.…”

Fifty years of shape grammars: A systematic mapping of its application in engineering and architecture

“…Of the 19 publications including optimisation, 13 also involve engineering; only six publications optimise for architectural purposes, of which four belong to floor plan [52][53][54][55] and the others belong to global shape. 32,56 Table 7 presents all the articles that include optimisation.…”

Machine learning for conservation of architectural heritage

“…, 1991). The development of advanced tools that can analyze architectural designs and break down architectural information into decomposable sub-components has become an objective (Gross, 1996; Chouchoulas and Day, 2007; Erem and Ermiyagil, 2016).…”

“…Frameworks for case-based, knowledge-based, or data-based design systems have been tried to be drawn (Coyne et al, 1990;Rosenman et al, 1991). The development of advanced tools that can analyze architectural designs and break down architectural information into decomposable sub-components has become an objective (Gross, 1996;Chouchoulas and Day, 2007;Erem and Ermiyagil, 2016).…”