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.
|Name||Advances in Intelligent Systems and Computing|
|Conference||6th International Symposium on Ambient Intelligence (ISAmI 2015)|
|Period||3/06/15 → 5/06/15|
- Ambient intelligence
- Evolutionary algorithms