Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs 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 : 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.
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Communication dans un congrès
Genetic and Evolutionary Computation Conference (GECCO 2017), 2017, Berlin, Germany. 2017, 〈10.1145/3071178.3071282〉
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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. 2017, 〈10.1145/3071178.3071282〉. 〈hal-01518698〉

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