Data-Efficient Design Exploration through Surrogate-Assisted Illumination

  • Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a two-dimensional airfoil optimization problem SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic three-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.

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Metadaten
Document Type:Article
Language:English
Parent Title (English):Evolutionary Computation
Volume:0
Pagenumber:30
ISSN:1063-6560
DOI:10.1162/evco_a_00231
Pubmed Id:29883202
Publisher:Massachusetts Institute of Technology
Date of first publication:2018/06/08
Submission status:Epub ahead of print
Tag:Computer Automated Design; MAP-Elites; Quality Diversity; Surrogate Modeling
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2018/06/13

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