Shape Optimization with Surface-Mapped CPPNs

Richards, Daniel and Amos, Martyn (2017) Shape Optimization with Surface-Mapped CPPNs. IEEE Transactions on Evolutionary Computation, 21 (3). pp. 391-407. ISSN 1089-778X

Richards, Amos - Shape Optimization with Surface-Mapped CPPNs AAM.pdf - Accepted Version

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Shape optimization techniques are becoming increasingly important in design and engineering. This growing significance reflects the need to exploit advances in digital fabrication technologies, and the desire to create new types of surface designs for various engineering applications. Evolutionary algorithms (EAs) offer several key advantages for shape optimization, but they can also be restricted, especially as design problems scale up in size. A key challenge for evolutionary shape optimization is to overcome these challenges in order to apply EAs to large-scale, “real-world” engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call “surface-mapped compositional pattern producing networks (CPPNs).” Our method outperforms a state-of-the-art gradient-based method on a simple benchmark problem, and scales well as degrees of freedom are added to the design problem. Our results demonstrate that surface-mapped CPPNs offer practical ways of approaching large-scale, real-world engineering problems with EAs, opening up exciting new opportunities for engineering design.

Item Type: Article
Uncontrolled Keywords: Compositional pattern producing network-neuroevolution of augmented topologies (CPPN-NEAT), engineering design, generative encodings, optimization methods, shape optimization.
Subjects: G400 Computer Science
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 18 Sep 2018 11:38
Last Modified: 31 Jul 2021 13:36

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