TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gaier, Adam
A1  - Asteroth, Alexander
A1  - Mouret, Jean-Baptiste
T1  - Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
T2  - GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017
N2  - The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined 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 different 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.
KW  - Quality Diversity
KW  - surrogate modeling
KW  - Aerodynamics
KW  - MAP-Elites
UR  - https://hal.inria.fr/hal-01518698
SN  - 978-1-4503-4920-8
SB  - 978-1-4503-4920-8
U6  - https://doi.org/10.1145/3071178.3071282
DO  - https://doi.org/10.1145/3071178.3071282
AX  - 1702.03713
SP  - 99
EP  - 106
PB  - Association for Computing Machinery
CY  - New York, NY, United States
ER  -