- author = "Philip Valencia and Aiden Haak and Alban Cotillon and Raja Jurdak",
- title = "Genetic programming for smart phone personalisation",
- journal = "Applied Soft Computing",
- year = "2014",
- volume = "25",
- pages = "86--96",
- month = dec,
- keywords = "genetic algorithms, genetic programming, genetic improvement, Island Model, Personalization, Smart phone, Online evolutionary",
- ISSN = "1568-4946",
-
URL = "
http://www.sciencedirect.com/science/article/pii/S1568494614004232",
-
DOI = "
10.1016/j.asoc.2014.08.058",
- size = "11 pages",
- abstract = "Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an on line learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smart phones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.",
-
notes = "p87 'injecting random programs ... can be beneficial
... energy-efficient...' AGP
http://sourceforge.net/projects/agpframework/ google
RSS reader, pop=5, max depth=3. Wifi energy, pop=12.
Collaboration two Samsung pop=10
Also \cite{Valencia:2014:arXiv}",
