Generative design processes were used in the architecture, engineering, and construction (AEC) industry long before modern machine learning (ML) approaches emerged. For almost three decades, genetic algorithms (GA) have helped generate populations of solutions that comply with design aspirations and performance criteria. Although these systems have proven useful, they are not widely implemented because of the computational power required to generate and run analyses for thousands of design options in a timely fashion. This research focuses on the development of a bespoke optimization system that addresses these issues by using a custom graphics processing unit (GPU) based analytical engine (Cyclops) and a proprietary distributed computing system (Hydra). Together, Cyclops and Hydra enable optimizations to scale up to the urban development level and converge in a fraction of the time it takes commercially available software. In addition, this article will discuss the development of applications that extend this workflow through the visualization and postprocessing of results, the potential of ML methods being integrated into the process, and how deep generative models can benefit from the outputs of this workflow.
