TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Gaier, Adam A1 - Asteroth, Alexander A1 - Mouret, Jean-Baptiste T1 - Data-Efficient Design Exploration through Surrogate-Assisted Illumination JF - Evolutionary Computation N2 - 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 two-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 three-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. KW - Computer Automated Design KW - Surrogate Modeling KW - Quality Diversity KW - MAP-Elites SN - 1063-6560 SS - 1063-6560 U6 - https://doi.org/10.1162/evco_a_00231 DO - https://doi.org/10.1162/evco_a_00231 PM - 29883202 AX - 1806.05865 VL - 26 IS - 3 SP - 381 EP - 410 PB - MIT Press ER -