Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence

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Abstract

As users can have greatly different preferences, the personalization of ambient devices is of utmost importance. Several approaches have been proposed to establish such a personalization in the form of machine learning or more dedicated knowledge-driven learning approaches. Despite its huge successes in optimization, evolutionary algorithms (EAs) have not been studied a lot in this context, mostly because it is known to be a slow learner. Currently however, quite fast EA based optimizers exist. In this paper, we investigate the suitability of EAs for ambient intelligence.
Original languageEnglish
Title of host publicationAmbient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015
EditorsA Mohamed, P Novais, A Pereira, G. Villarrubia-Gonzalez, A. Fernandez-Caballero
PublisherSpringer/Verlag
Pages1-11
Number of pages11
Volume376
ISBN (Electronic)9783319196947
DOIs
Publication statusPublished - 2015
Event6th International Symposium on Ambient Intelligence (ISAmI 2015) - Salamanca, Spain
Duration: 3 Jun 20155 Jun 2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume376
ISSN (Print)2194-5357

Conference

Conference6th International Symposium on Ambient Intelligence (ISAmI 2015)
CountrySpain
CitySalamanca
Period3/06/155/06/15

Keywords

  • Ambient intelligence
  • CMA-ES
  • Evolutionary algorithms
  • Personalization

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  • Cite this

    Gao, S., & Hoogendoorn, M. (2015). Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence. In A. Mohamed, P. Novais, A. Pereira, G. Villarrubia-Gonzalez, & A. Fernandez-Caballero (Eds.), Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015 (Vol. 376, pp. 1-11). (Advances in Intelligent Systems and Computing; Vol. 376). Springer/Verlag. https://doi.org/10.1007/978-3-319-19695-4_1