On Design Mining: Coevolution and Surrogate Models

Preen, Richard; Bull, Larry

Abstract

© 2017 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.

• Journal Article Type: Article
• Acceptance Date: Feb 2, 2017
• Publication Date: May 1, 2017
• Deposit Date: Feb 8, 2017
• Publicly Available Date: Jun 2, 2017
• Journal: Artificial Life
• Print ISSN: 1064-5462
• Electronic ISSN: 1530-9185
• Publisher: Massachusetts Institute of Technology Press (MIT Press)
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 2
• Pages: 186-205
• DOI: https://doi.org/10.1162/ARTL_a_00225
• Keywords: 3D printing, coevolution, shape optimisation, surrogate models, turbine, wind energy
• Public URL: https://uwe-repository.worktribe.com/output/895931
• Publisher URL: http://www.mitpressjournals.org/doi/full/10.1162/ARTL_a_00225
• Additional Information: Additional Information : The dataset for this study is available from the UWE Research Data Repository: http://researchdata.uwe.ac.uk/166
• Contract Date: Feb 8, 2017