1. Android Genetic Programming Framework Alban Cotillon Philip Valencia Raja Jurdak CSIRO ICT Centre, Brisbane, Australia

2. Why GP for Smart Phone Personalisation Why personalize smart phones? Exponential market growth in recent years Diverse user preferences and contexts Broad range of embedded sensors Access to huge Internet data base High* computation power Why use online GP? Limitations of rule-based approaches Need adaptation to new contexts Need to continuously evolve Why Android? Open-source Support Java development Quick prototyping Paper goal To demonstrate AGP’s ability to solve multi- objective problems in dynamic environments

3. Design Considerations – Interaction and Resources User interaction Direct access to Android API (no scripting language) Developer-specified permissions Limited resources Computation Energy Android API battery level System file to track consumption Key idea Learn more when there is more slack

4. Design Considerations – Services and Intent Services Long running background operations through Android services Interpreter Shell Processes generated programs Runs in separate thread from main application Avoid ANR errors for buggy programs No direct access to sensors Interpreter context keeps track of which sensors have been used for this application Avoids powering off sensors needed by other applications

5. The AGP Framework

6. AGP Data Structures and Populations Functions and Terminals AGP-specific interfaces FunctionInterface and TerminalInterface Specify primitive arity, string serialization representation, estimated time cost Use Strategy Pattern for flexibility FunctionSet and TerminalSet Store available functions and terminals for application Developer can add, remove or search primitives Populations and Programs Use Builder Pattern Implemented as trees Access to function and terminal sets, and helper

7. Selectors, Genetic Operators, and State Selectors Evaluation thread ranks running programs in execution thread Top-ranked programs chosen to breed next generation Use wheel selectors by default Currently supports two genetic operators Crossover Mutation Saving program and population state Needed for undesired reboots, crashes, battery depletion Use serializable classes Specialised builders to reconstruct populations and programs from serialized form saved in a file UnserializePopulationBuilder UnserializeProgramBuilder

8. Injecting Expert Knowledge Constrain evolution landscape Needed to avoid resource overutilization AGP supports two components Helper Called during program generation process One or more per application using HelperInterface Use evaluate() function to specify correctness conditions for application E.g.: discard program for geolocalization that do not call a location service Supervisor Runs during program interpretation Check constraints on-the-fly Can kill Interpreter Shell if constraints exceeded E.g.: enforce limits on program execution time

9. Case Study: Google Reader Application Provides selected news feeds to users Preferred content is context-specific for Smart Phones Less text More photos Whenever the user wants to get news, she asks for a news report which executes a GP program and returns the latest and unread news from feeds selected by the program.

10. Experiments and Results 7 news sources 4 technology news websites 1 infographics Break Videos Business Green for latest green products User has entered interest for all sites Preference for technology news sites on smart phone Desired story count set to 10 Pool size is 5 programs

11. Case Study: Context-aware Localization Smart Phones provide several location data sources GPS Cell-tower Wifi Context-specific cost benefit for each technology Position Signal quality Device energy profile

12. Accuracy Fitness During learning Evaluation thread keeps all location providers on Uses provider with best accuracy as best position

13. Energy Fitness Assumptions Assume day-long operation Use 1400mAh battery capacity Target average current draw of 63 mA for 22 hours Implementation Use Android PowerProfile class Assess energy cost based on selected location provider and CPU usage Energy fitness is a linear function between 0 and 1 0 means the program energy cost will not meet the daily operation target 1 means the program costs no energy

14. Experiments and Results 12 programs/population Evaluation time is 1 minute Function set provides arithmetic functions and location provider selection functions Two sets of experiments with and without Helper

15. Discussion and Conclusions Android Genetic Programming Framework Smart phone personalization through online GP Demonstrated on two case study applications Diversity/usability considerations Future work Cooperative evolution through the Island Model

16. Thank you Dr. Raja Jurdak CSIRO ICT Centre Principal Research Scientist Research Group Leader Phone: +61 (0)7 3327 4059 Email: raja.jurdak@csiro.au Web: http://jurdak.com University of Queensland Adjunct Associate Professor