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PDF 1.5 MB
DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch042
Pages 255-262
First published 30 July 2014

Evolving Morphologies with CPPN-NEAT and a Dynamic Substrate

Daniel Richards and Martyn Amos

Abstract (Excerpt)

Recent advances in fabrication technologies open up exciting opportunities to manufacture entirely new types of physical materials and structures. These have varied and specific mechanical properties, which can be exploited in a number of engineering applications, and a growing area of research concerns the generation of two- and three-dimensional designs using these materials. However, the computational tools required to explore large spaces of possible 2-D and 3-D morphologies remain underdeveloped. State-of-the-art evolutionary approaches such as CPPN-NEAT and HyperNEAT-LEO are often used to explore possible 2-D and 3-D designs, but their ability to construct efficient solutions for practical use in engineering domains remains in question. In this paper, we present an extension of CPPN-NEAT, in which nodes grow connections across a dynamic substrate, and illustrate this by creating efficient 2-D truss structures. Using four benchmark problems, we then demonstrate that our extended CPPN-NEAT model outperforms similar HyperNEAT methods for approximating specific connectivity patterns, and suggests important clues regarding how to best harness generative and developmental representations to build scalable and high-performance physical morphologies.