Deep Parameter Optimisation on Android Smartphones for Energy Minimisation - A Tale of Woe and a Proof-of-Concept

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

@InProceedings{Bokhari:2017:GI,
  author =       "Mahmoud A. Bokhari and Bobby R. Bruce and 
                 Brad Alexander and Markus Wagner",
  title =        "Deep Parameter Optimisation on Android Smartphones for
                 Energy Minimisation - A Tale of Woe and a
                 Proof-of-Concept",
  booktitle =    "GI-2017",
  year =         "2017",
  editor =       "Justyna Petke and David R. White and W. B. Langdon and 
                 Westley Weimer",
  pages =        "1501--1508",
  address =      "Berlin",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, non-functional properties, mobile devices,
                 multi-objective optimisation, dreaming smartphone,
                 Android 6",
  isbn13 =       "978-1-4503-4939-0",
  URL =          "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/bokhari2017_deep_parameter_optimisation.pdf",
  URL =          "http://cs.adelaide.edu.au/~markus/pub/2017gecco-deepandroid.pdf",
  DOI =          "doi:10.1145/3067695.3082519",
  size =         "8 pages",
  abstract =     "With power demands of mobile devices rising, it is
                 becoming increasingly important to make mobile software
                 applications more energy efficient. Unfortunately,
                 mobile platforms are diverse and very complex which
                 makes energy behaviours difficult to model. This
                 complexity presents challenges to the effectiveness of
                 off-line optimisation of mobile applications. In this
                 paper, we demonstrate that it is possible to
                 automatically optimise an application for energy on a
                 mobile device by evaluating energy consumption in-vivo.
                 In contrast to previous work, we use only the device's
                 own internal meter. Our approach involves many
                 technical challenges but represents a realistic path
                 toward learning hardware specific energy models for
                 program code features.",
  notes =        "Rebound, Java, 44 test cases with test oracles.
                 Sensativity analysis of all integer and double
                 constants before start reduces 38 paramters to 19.
                 NSGA-II. Sample remaining energy in battery four times
                 a second. flight mode not sufficient. Contrel
                 temperature. Nexus 6, Nexus 9. Fixed CPU closck speed.
                 Java garbage collector alos run 4 times
                 persecond.Kolmogorov-Smirnov normality test. Recharge
                 battery between generations. 55 mutants",
}

Genetic Programming entries for Mahmoud A Bokhari Bobby R Bruce Brad Alexander Markus Wagner

Citations