An evolution-based generative design system : using adaptation to shape architectural form

TL;DR: Norford et al. as mentioned in this paper introduced a Generative Design System (GS) that draws on evolutionary concepts to incorporate adaptation paradigms into the architectural design process and applied it to an existing building by Alvaro Siza.

Abstract: This dissertation dwells in the interstitial spaces between the fields of architecture, environmental design and computation. It introduces a Generative Design System that draws on evolutionary concepts to incorporate adaptation paradigms into the architectural design process. The initial aim of the project focused on helping architects improving the environmental performance of buildings, but the final conclusions of the thesis transcend this realm to question the process of incorporating computational generative systems in the broader context of architectural design. The Generative System [GS] uses a Genetic Algorithm as the search and optimization engine. The evaluation of solutions in terms of environmental performance is done using DOE2.1E. The GS is first tested within a restricted domain, where the optimal solution is previously known, to allow for the evaluation of the system's performance in locating high quality solutions. Results are very satisfactory and provide confidence to extend the GS to complex building layouts. Comparative studies using other heuristic search procedures like Simulated Annealing are also performed. The GS is then applied to an existing building by Alvaro Siza, to study the system's behavior in a complex architectural domain, and to assess its capability for encoding language constraints, so that solutions generated may be within certain design intentions. An extension to multicriteria problems is presented, using a Pareto-based method. The GS successfully finds well-defined Pareto fronts providing information on best trade-offs between conflicting objectives. The method is open-ended, as it leaves the final decision-making to the architect. Examples include finding best trade-offs between costs of construction materials, annual energy consumption in buildings, and greenhouse gas emissions embedded in materials. The GS is then used to generate whole building geometries, departing from abstract relationships between design elements and using adaptation to evolve architectural form. The shape-generation experiments are performed for distinct geographic locations, testing the algorithm's ability to adapt buildings shape to different environments. Pareto methods are used to investigate what forms respond better to conflicting objectives. New directions of research are suggested, like combining the GS with a parametric solid modeler, or extending the investigation to the study of complex adaptive systems in architecture. Thesis Supervisor: Leslie K. Norford Title: Associate Professor of Building Technology, Department of Architecture