Design optimization techniques are often used at the beginning of the design
process to explore the space of possible designs. In these domains illumination
algorithms, such as MAP-Elites, are promising alternatives to classic
optimization algorithms because they produce diverse, high-quality solutions in
a single run, instead of only a single near-optimal solution. Unfortunately,
these algorithms currently require a large number of function evaluations,
limiting their applicability. In this article we introduce a new illumination
algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate
modeling techniques to create a map of the design space according to
user-defined features while minimizing the number of fitness evaluations. On a
2-dimensional airfoil optimization problem SAIL produces hundreds of diverse
but high-performing designs with several orders of magnitude fewer evaluations
than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of
producing maps of high-performing designs in realistic 3-dimensional
aerodynamic tasks with an accurate flow simulation. Data-efficient design
exploration with SAIL can help designers understand what is possible, beyond
what is optimal, by considering more than pure objective-based optimization.

本文介绍了一种新的启发式算法 ——Surrogate-Assisted Illumination，利用代理建模技术在设计空间中创建映射，同时最小化适应度评估次数。使用这个算法进行设计优化，可以在更少的评估次数内产生高质量、多样性的设计，有助于设计者了解超越纯粹目标优化之外的可能性。