Genetic programming for smart phone personalisation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Valencia:2014:ASC,
  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, Island Model,
                 Personalization, Smart phone, Online evolutionary",
  ISSN =         "1568-4946",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494614004232",
  DOI =          "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}",
}

Genetic Programming entries for Philip Valencia Aiden Haak Alban Cotillon Raja Jurdak

Citations