loomis - Benjamin Loomis Working Paper SGGA A NOTE ON...

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Benjamin Loomis Working Paper SGGA 1 A NOTE ON GENERATIVE DESIGN TECHNIQUES: S G G A A USER-DRIVEN GENETIC ALGORITHM FOR EVOLVING NON-DETERMINISTIC SHAPE GRAMMARS Benjamin A Loomis Massachusetts Institute of Technology, Cambridge MA, USA ABSTRACT We propose a model for generative design which synthesizes two separate but well-established forms of computational design. It is argued that shape grammars and genetic algorithms address complementary aspects of the generative design problem, and that injecting the theory and research from each field into the other will promote the development of better generative design machines. We also present a prototypical system which synthesizes these strands of research, sketched out within AutoCAD's programming environment. Note: the included Figures and Tables are mock-ups only
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Benjamin Loomis Working Paper SGGA 2 INTRODUCTION Efforts in computational design can largely be characterized as stemming from one of two positions, which might be referred to as the logical and biological. Broadly speaking, the logical strand of research focuses on systems of production and analysis, such as grammars, syntaxes, and similar rigorously defined languages. The biological strand of research, on the other hand, tends to focus on adaptation and evolution, along with the related processes of complexity, self-organization, and emergence. Among the work in each of these strands of thought about computational design methods, shape grammars and genetic algorithms stand out as two of the most well- established fields of research in their respective domains. Both first made known in the early seventies, shape grammars and genetic algorithms have each established a solid body of knowledge and community of researchers over the past thirty years. Shape grammars have proven capable of producing complex and meaningful design languages, as exemplified by the Palladian, Prairie, and Queen Anne house grammars, and are theoretically capable of producing any design (Stiny, 1975). Similarly, genetic algorithms are recognized as a powerful and robust problem- solving method, with a wide range of theorems and applications which suggest their optimality for many types of problems (Goldberg, 1989; Mitchell, 1996). However, as the two areas of research stand on opposite poles of the computational design spectrum, the few examples of explicitly combining grammars and search algorithms (Shea, 1998; Rosenman and Gero, 1999) have yet to incorporate the established fields of shape grammars and genetic algorithms. In the field of genetic algorithms and other evolutionary computing techniques, for example, it is often thought that shape grammars are too simplistic or restrictive to make use of the full potential inherent in the
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Benjamin Loomis Working Paper SGGA 3 evolutionary paradigm (Frazer, 1995). Similarly, it might be argued that the structure of genetic algorithms do not lend themselves to the full range of possibilities inherent in visual calculation, or that the evolutionary approach tends to focus on results to the
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loomis - Benjamin Loomis Working Paper SGGA A NOTE ON...

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