Search-Based Energy Optimization of Some Ubiquitous Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Brownlee:2017:ieeeETCI,
  author =       "Alexander Edward Ian Brownlee and Nathan Burles and 
                 Jerry Swan",
  title =        "Search-Based Energy Optimization of Some Ubiquitous
                 Algorithms",
  journal =      "IEEE Transactions on Emerging Topics in Computational
                 Intelligence",
  year =         "2017",
  volume =       "1",
  number =       "3",
  pages =        "188--201",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, SBSE",
  ISSN =         "2471-285X",
  DOI =          "doi:10.1109/TETCI.2017.2699193",
  abstract =     "Reducing computational energy consumption is of
                 growing importance, particularly at the extremes (i.e.,
                 mobile devices and datacentres). Despite the ubiquity
                 of the Java virtual machine (JVM), very little work has
                 been done to apply search-based software engineering
                 (SBSE) to minimize the energy consumption of programs
                 that run on it. We describe OPACITOR, a tool for
                 measuring the energy consumption of JVM programs using
                 a bytecode level model of energy cost. This has several
                 advantages over time-based energy approximations or
                 hardware measurements. It is 1) deterministic, 2)
                 unaffected by the rest of the computational
                 environment, 3) able to detect small changes in
                 execution profile, making it highly amenable to
                 metaheuristic search, which requires locality of
                 representation. We show how generic SBSE approaches
                 coupled with OPACITOR achieve substantial energy
                 savings for three widely used software components.
                 Multilayer perceptron implementations minimizing both
                 energy and error were found, and energy reductions of
                 up to 70percent and 39.85percent were obtained over the
                 original code for Quicksort and object-oriented
                 container classes, respectively. These highlight three
                 important considerations for automatically reducing
                 computational energy: tuning software to particular
                 distributions of data; trading off energy use against
                 functional properties; and handling internal
                 dependencies that can exist within software that render
                 simple sweeps over program variants sub-optimal.
                 Against these, global search greatly simplifies the
                 developer's job, freeing development time for other
                 tasks.",
  notes =        "Also known as \cite{7935484}",
}

Genetic Programming entries for Alexander E I Brownlee Nathan Burles Jerry Swan

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