Abstract
In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we could quickly understand the complex behaviors that can emerge in said domains instead? We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm which allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem. A fast GPU-based fluid dynamics solver is used in conjunction with surrogate models to accurately predict fluid characteristics from the shapes that produce the air flow. We show that these features can be modeled in a data-driven way while sampling to improve performance, rather than explicitly sampling to improve feature models. Our method can reduce the need to run an infeasibly large set of simulations while still being able to design a large diversity of air flows and the shapes that cause them. Discovering diversity of behaviors helps engineers to better understand expensive domains and their solutions.
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Acknowledgments
This work was funded by the Ministry for Culture and Science of the state of Northrhine-Westphalia (grant agreement no. 13FH156IN6) and the German Research Foundation (DFG) project FO 674/17-1. The authors thank Andreas Krämer for the discussions about the Lettuce solver.
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Hagg, A., Wilde, D., Asteroth, A., Bäck, T. (2020). Designing Air Flow with Surrogate-Assisted Phenotypic Niching. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12269. Springer, Cham. https://doi.org/10.1007/978-3-030-58112-1_10
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DOI: https://doi.org/10.1007/978-3-030-58112-1_10
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