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Journal Articles Evolutionary Computation Year : 2018

Data-Efficient Design Exploration through Surrogate-Assisted Illumination


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.
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hal-01817505 , version 1 (18-06-2018)



Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret. Data-Efficient Design Exploration through Surrogate-Assisted Illumination. Evolutionary Computation, 2018, 26 (3), pp.381-410. ⟨10.1162/evco_a_00231⟩. ⟨hal-01817505⟩
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