ABSTRACT

Urban Design is a mult i-objective task. Traditionally, urban spaces are designed hierarchically; organizational

inputs are idealized uniquely, and negotiated through sequential overlay. In our investigation, parametric

modeling (with the software application Catia) and evolutionary optimizat ion employing genetic algorithms

(with the software application Mode Frontier) enable the explorat ion of a non-linear design space whereby

multiple objectives may be optimized concurrently. This paper describes an experiment that builds from

prior research in multi-objective optimizat ion of architectural design and applies that workflow to multiobjective

optimizat ion in urban des ign. The experiment employs given constraints, custom procedural

algorithms and genet ic algorithms to examine a wide design space and identify des igns that perform well in

multiple arenas. Design, data and latent influences are exposed and negotiated quantitatively to render

topological var iat ion through optimizat ion. By using multi-objective optimizat ion we define and apply

quantitative metrics in order to examine the potential for a new workflow in urban design. 

Keywords: Optimizat ion, Catia, Mode Frontier