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

## TL;DR

This paper introduces SAIL, a surrogate-assisted illumination algorithm that enhances the MAP-Elites method by reducing evaluations needed to find diverse, high-performing designs, demonstrated on a 2D airfoil problem.

## Contribution

SAIL integrates surrogate models with MAP-Elites to efficiently explore design spaces, significantly reducing the number of evaluations compared to traditional methods.

## Key findings

- SAIL produces better solutions than MAP-Elites in fewer evaluations.
- SAIL finds high-quality solutions across all bins in the design space.
- SAIL requires several orders of magnitude fewer evaluations than standard methods.

## Abstract

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), 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.