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This project attempts to utilize generative software in order to create an analytical system intended to enhance order and efficiency within groups of complexly inter-connected architectural programs. The focus of the research involves developing a parametric definition that can diagrammatically arrange spatial volumes. These volumes represent the various building functions (programs) in terms of square footage. The driving condition for these experiments is adjacency. The theory is that if two functions in a building need to be connected, they should be adjacent to one another. The degree of adjacency is here viewed as the distance between the centers of programmatic masses. The relationship between programs is scored as a numerical value, the lower the value, the closer the programs, the better the score. The resulting model contains all programs, arranged within a larger volumetric container. This container represents the building shell. Each arrangement represents an ‘individual’ iteration. The tool will automatically create individuals and store their information in groups or 'generations’. The evolutionary process improves the proximity score over the course of many generations. The resulting diagrams produced autonomously by the computer are sorted, and the fittest results can be examined and refined in sharp contrast to less fit ones, informing the design process. This type of approach can offer a much wider range of explorations in a very short window of time than conventionally possible.
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