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Shape optimization with surface-mapped CPPNs

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>06/2017
<mark>Journal</mark>IEEE Transactions on Evolutionary Computation
Issue number3
Volume21
Number of pages17
Pages (from-to)391-407
Publication statusPublished
Early online date2/09/16
Original languageEnglish

Abstract

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 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 evolutionary algorithms to large-scale, "real-world" engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call "surface-mapped 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 evolutionary algorithms, opening up exciting new opportunities for engineering design.

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©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.