Data-Efficient Design Exploration through Surrogate-Assisted Illumination

Adam Gaier 1, 2 Alexander Asteroth 1 Jean-Baptiste Mouret 2
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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 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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Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret. Data-Efficient Design Exploration through Surrogate-Assisted Illumination. Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2018, 26 (3), pp.381-410. ⟨https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00231?journalCode=evco⟩. ⟨10.1162/evcoa_00231⟩. ⟨hal-01817505⟩

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