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.
Liste complète des métadonnées

Cited literature [25 references]  Display  Hide  Download

https://hal.inria.fr/hal-01518698
Contributor : Jean-Baptiste Mouret <>
Submitted on : Friday, May 5, 2017 - 11:16:21 AM
Last modification on : Tuesday, December 18, 2018 - 4:40:22 PM
Document(s) archivé(s) le : Sunday, August 6, 2017 - 12:37:56 PM

File

sail2017.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

608

Files downloads

409