Abstract
| 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 | |
| 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 | Spain |
| City | Salamanca |
| Period | 3/06/15 → 5/06/15 |
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Keywords
- Ambient intelligence
- CMA-ES
- Evolutionary algorithms
- Personalization
Cite this
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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
TY - GEN
T1 - Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence
AU - Gao, Shu
AU - Hoogendoorn, Mark
PY - 2015
Y1 - 2015
N2 - 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.
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.
KW - Ambient intelligence
KW - CMA-ES
KW - Evolutionary algorithms
KW - Personalization
UR - http://www.scopus.com/inward/record.url?scp=84937410498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937410498&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19695-4_1
DO - 10.1007/978-3-319-19695-4_1
M3 - Conference contribution
VL - 376
T3 - Advances in Intelligent Systems and Computing
SP - 1
EP - 11
BT - Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015
A2 - Mohamed, A
A2 - Novais, P
A2 - Pereira, A
A2 - Villarrubia-Gonzalez, G.
A2 - Fernandez-Caballero, A.
PB - Springer/Verlag
ER -