Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence

Shu Gao; Mark Hoogendoorn

doi:10.1007/978-3-319-19695-4_1

Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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 language	English
Title of host publication	Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015
Editors	A Mohamed, P Novais, A Pereira, G. Villarrubia-Gonzalez, A. Fernandez-Caballero
Publisher	Springer/Verlag
Pages	1-11
Number of pages	11
Volume	376
ISBN (Electronic)	9783319196947
DOIs	https://doi.org/10.1007/978-3-319-19695-4_1
Publication status	Published - 2015
Event	6th International Symposium on Ambient Intelligence (ISAmI 2015) - Salamanca, Spain Duration: 3 Jun 2015 → 5 Jun 2015

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	376
ISSN (Print)	2194-5357

Conference

Conference	6th International Symposium on Ambient Intelligence (ISAmI 2015)
Country/Territory	Spain
City	Salamanca
Period	3/06/15 → 5/06/15

Keywords

Ambient intelligence
CMA-ES
Evolutionary algorithms
Personalization

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10.1007/978-3-319-19695-4_1

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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

Gao, Shu ; Hoogendoorn, Mark. / Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence. Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015. editor / A Mohamed ; P Novais ; A Pereira ; G. Villarrubia-Gonzalez ; A. Fernandez-Caballero. Vol. 376 Springer/Verlag, 2015. pp. 1-11 (Advances in Intelligent Systems and Computing).

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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.",

keywords = "Ambient intelligence, CMA-ES, Evolutionary algorithms, Personalization",

author = "Shu Gao and Mark Hoogendoorn",

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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, Advances in Intelligent Systems and Computing, vol. 376, Springer/Verlag, pp. 1-11, 6th International Symposium on Ambient Intelligence (ISAmI 2015), Salamanca, Spain, 3/06/15. https://doi.org/10.1007/978-3-319-19695-4_1

Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence. / Gao, Shu; Hoogendoorn, Mark.

Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015. ed. / A Mohamed; P Novais; A Pereira; G. Villarrubia-Gonzalez; A. Fernandez-Caballero. Vol. 376 Springer/Verlag, 2015. p. 1-11 (Advances in Intelligent Systems and Computing; Vol. 376).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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

Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence

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