CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

id acadia12_67
authors Gerber, Dr. David Jason ; Lin, Shih-Hsin
year 2012
title Synthesizing Design Performance: An Evolutionary Approach to Multidisciplinary Design Search
source ACADIA 12: Synthetic Digital Ecologies [Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-1-62407-267-3] San Francisco 18-21 October, 2012), pp. 67-75
summary Design is a goal oriented decision-making activity. Design is ill defined and requiring of synthetic approaches to weighing and understanding tradeoffs amongst soft and hard objectives, and the imprecise and or computationally explicit criteria and goals. In this regard designers in contemporary practice face a crisis of sorts. How do we achieve performance under large degrees of uncertainty and limited design cycle time? How do we better design for integrating performance? Fundamentally design teams, are not typically given enough time nor the best tools to design explore, to generate design alternatives, and then evolve solution quality to search for best fit through expansive design solution spaces. Given the complex criteria for defining performance in architecture our research approach experiments upon an evolutionary and integrative computational strategy to expand the solution space of a design problem as well as pre-sort and qualify candidate designs. We present technology and methodology that supports rapid development of design problem solution spaces in which three design domains objectives have multi-directional impact on each other. The research describes the use of an evolutionary approach in which a genetic algorithm is used as a means to automate the design alternative population as well as to facilitate multidisciplinary design domain optimization. The paper provides a technical description of the prototype design, one that integrates associative parametric modeling with an energy use intensity evaluation and with a financial pro forma. The initial results of the research are presented and analyzed including impacts on design process; the impacts on design uncertainty and design cycle latency; and the affordances for ‘designing-in’ performance and managing project complexity. A summary discussion is developed which describes a future cloud implementation and the future extensions into other domains, scales, tectonic and system detail.
keywords Parametric Design , Domain Integration , Design Methods , Multidisciplinary Design Optimization (MDO) , Evolutionary Algorithms , Design Decision Support , Generative Design
series ACADIA
type normal paper
email dgerber@usc.edu
full text file.pdf (269,770 bytes)
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