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_id ecaade2016_163
id ecaade2016_163
authors Harding, John
year 2016
title Evolving Parametric Models using Genetic Programming with Artificial Selection
doi https://doi.org/10.52842/conf.ecaade.2016.1.423
source Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 423-432
summary Evolutionary methods with artificial selection have been shown to be an effective human-computer technique for exploring design spaces with unknown goals. This paper investigates an interactive evolution of visual programs currently used in popular parametric modelling software. Although parametric models provide a useful cognitive artifact for designers to interact with, they are often bound by their topological structure with the designer left to adjusting (or optimising) metric variables as part of a design search. By allowing the topological structure of the graph to be evolved as well as the parameters, artificial selection can be employed to explore a wider design space more suited to the early design stage.
wos WOS:000402063700047
keywords genetic programming; parametric design; artificial selection; evolutionary design; design exploration
series eCAADe
email
last changed 2022/06/07 07:49

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