Android Genetic Programming Framework

Personalisation in smart phones requires adaptability to dynamic context based on application usage and sensor inputs. Current personalisation approaches do not provide sufficient adaptability to dynamic and unexpected context. This paper introduces the Android Genetic Programming Framework (AGP) as a personalisation method for smart phones. AGP considers the specific design challenges of smart phones, such as resource limitation and constrained programming environments. We demonstrate AGP's utility through empirical experiments on two applications: a news reader application and an energy efficient localisation application. Results show that AGP successfully adapts application behaviour to user context.

[1]  Deborah Estrin,et al.  Diversity in smartphone usage , 2010, MobiSys '10.

[2]  Edward A. Fox,et al.  A genetic programming framework for content-based image retrieval , 2009, Pattern Recognit..

[3]  Weiguo Fan,et al.  Learning to advertise , 2006, SIGIR.

[4]  César Hervás-Martínez,et al.  JCLEC: a Java framework for evolutionary computation , 2007, Soft Comput..

[5]  Tom Lenaerts,et al.  Building a Genetic Programming Framework: The Added-Value of Design Patterns , 1998, EuroGP.

[6]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

[7]  Ismail A. Ismail,et al.  Genetic Programming Framework for Fingerprint Matching , 2009, ArXiv.

[8]  Raja Jurdak,et al.  Distributed genetic evolution in WSN , 2010, IPSN '10.

[9]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[10]  Ivan Tanev,et al.  XML-based genetic programming framework: design philosophy, implementation, and applications , 2010, Artificial Life and Robotics.

[11]  M. Nicholas,et al.  Sutherland: An extensible object-oriented software framework for evolutionary computation , 1998 .

[12]  Luís Ferreira Pires,et al.  Designing a configurable services platform for mobile context-aware applications , 2005, Int. J. Pervasive Comput. Commun..

[13]  Marc Parizeau,et al.  Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computation , 2002, GECCO Late Breaking Papers.

[14]  Hitoshi Iba,et al.  Genetic Programming 1998: Proceedings of the Third Annual Conference , 1999, IEEE Trans. Evol. Comput..

[15]  James P. Cohoon,et al.  C6.3 Island (migration) models: evolutionary algorithms based on punctuated equilibria , 1997 .

[16]  Giandomenico Spezzano,et al.  P-CAGE: An Environment for Evolutionary Computation in Peer-to-Peer Systems , 2006, EuroGP.

[17]  Letizia Tanca,et al.  A methodology for preference-based personalization of contextual data , 2009, EDBT '09.

[18]  Peter I. Corke,et al.  Adaptive GPS duty cycling and radio ranging for energy-efficient localization , 2010, SenSys '10.

[19]  James E. Rumbaugh,et al.  Object-Oriented Modelling and Design , 1991 .

[20]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[21]  Seungwan Ryu,et al.  Next Generation Mobile Service Environment and Evolution of Context Aware Services , 2006, EUC.

[22]  Kurt Geihs,et al.  An Adaptable Framework for Distributed Multi-Objective Search Algorithms Applied to the Genetic Programming of Sensor Networks , 2006 .

[23]  Feng Zhao,et al.  Energy-accuracy trade-off for continuous mobile device location , 2010, MobiSys '10.