Journal
ARTIFICIAL LIFE
Volume 23, Issue 2, Pages 186-205Publisher
MIT PRESS
DOI: 10.1162/ARTL_a_00225
Keywords
3D printing; coevolution; shape optimization; surrogate models; turbine; wind energy
Funding
- Engineering and Physical Sciences Research Council [EP/N005740/1]
- Leverhulme Trust [RPG-2013-344]
- Engineering and Physical Sciences Research Council [EP/N005740/1] Funding Source: researchfish
- EPSRC [EP/N005740/1] Funding Source: UKRI
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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.
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