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Hierarchical Surrogate Modeling for Illumination Algorithms

  • Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.

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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017
First Page:1407
Last Page:1410
ISBN:978-1-4503-4939-0
DOI:https://doi.org/10.1145/3067695.3082495
ArXiv Id:http://arxiv.org/abs/1703.09926
Publisher:ACM
Date of first publication:2017/07/01
Tag:bagging; evolutionary illumination; surrogate modeling
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2017/04/26