Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination

Adam Gaier (1, 2) , Alexander Asteroth (1) , Jean-Baptiste Mouret (2)

1 H-BRS - Hochschule Bonn Rhein-Sieg University of Applied Sciences [Sankt Augustin]
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features deened by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites. The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a diierent combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.

Mots clés: MAP-Elites; Surrogate Modeling; Quality Diversity; Aerodynamics

Domaines: Intelligence artificielle [cs.AI]; Optimisation et contrôle [math.OC]; Mécanique des fluides [physics.class-ph]

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret. Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination. Genetic and Evolutionary Computation Conference (GECCO 2017), 2017, Berlin, Germany. ⟨10.1145/3071178.3071282⟩. ⟨hal-01518698⟩