@article{GaierAsterothMouret2018,
  author    = {Adam Gaier and Alexander Asteroth and Jean-Baptiste Mouret},
  title     = {Data-Efficient Design Exploration through Surrogate-Assisted Illumination},
  series   = {Evolutionary Computation},
  volume    = {26},
  number    = {3},
  publisher = {MIT Press},
  issn      = {1063-6560},
  doi       = {10.1162/evco\_a\_00231},
  pages     = {381 -- 410},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}