Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 18 of 64
Back to Result List

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

  • 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.

Export metadata

Additional Services

Search Google Scholar Check availability

Statistics

Show usage statistics
Metadaten
Document Type:Conference Object
Language:English
Author:Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Parent Title (English):GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017
First Page:99
Last Page:106
ISBN:978-1-4503-4920-8
URL:https://hal.inria.fr/hal-01518698
DOI:https://doi.org/10.1145/3071178.3071282
ArXiv Id:http://arxiv.org/abs/1702.03713
Publisher:Association for Computing Machinery
Place of publication:New York, NY, United States
Date of first publication:2017/07/01
Award:Best paper award in the complex systems category
Copyright:© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. Abstracting with credit is permitted.
Funding:This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 637972, project "ResiBots") and the German Federal Ministry of Education and Research (BMBF) under the Forschung an Fachhochschulen mit Unternehmen programme (grant agreement number 03FH012PX5 project "Aeromat").
Keyword:Aerodynamics; MAP-Elites; Quality Diversity; surrogate modeling
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Projects:AErOMAt - Automatisiertes Entwickeln aerodynamischer Strukturen und Fahrzeuge mithilfe evolutionärer Optimierung und Surrogatmodellierung (DE/BMBF/03FH012PX5,13FH012PX5)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2017/05/11