Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence
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.
KeywordsAmbient intelligence Evolutionary algorithms Personalization CMA-ES
This research is supported in part by scholarship from China Scholarship Council under number 201304910373. Furthermore, we would like to thank Gusz Eiben for the fruitful discussions and the anonymous reviewers for they valuable comments that helped to improve the paper.
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