Skip to main content

Research Repository

Advanced Search

Evolving parametric models using genetic programming with artificial selection

Harding, John

Authors

John Harding John3.Harding@uwe.ac.uk
External Adviser / Panel Member - SAS



Abstract

Evolutionary methods with artificial selection have been shown to be a useful computer-human technique for exploring wide design spaces with unknown goals. This paper investigates a similar approach in the evolution of visual programs currently used in popular parametric modelling software. Although associative models provide a useful cognitive artifact for the designer to interact with, they are often bound by their topological structure with the designer left to adjusting (or optimising) variables to explore the design space. By allowing the topological structure of the graph to be evolved as well as the parameters, artificial selection can be employed to explore a wide design space more suited to the early design stage.

Citation

Harding, J. (2016, August). Evolving parametric models using genetic programming with artificial selection. Paper presented at 34th eCAADe Conference, 2016, Oulu, Finland

Presentation Conference Type Conference Paper (unpublished)
Conference Name 34th eCAADe Conference, 2016
Conference Location Oulu, Finland
Start Date Aug 22, 2016
End Date Aug 26, 2016
Acceptance Date Mar 30, 2016
Publication Date Jan 1, 2016
Peer Reviewed Peer Reviewed
Pages 423-432
Keywords genetic programming, parametric design, artificial selection, evolutionary design, design exploration
Public URL https://uwe-repository.worktribe.com/output/919520
Publisher URL http://papers.cumincad.org/cgi-bin/works/Show?ecaade2016_163
Additional Information Additional Information : Paper can be found at: http://papers.cumincad.org/cgi-bin/works/Show?ecaade2016_163
Title of Conference or Conference Proceedings : Proceedings of the 34th eCAADe Conference, 2016