Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Nathan Burles and Edward Bowles and 
                 Alexander E. I. Brownlee and Zoltan A. Kocsis and Jerry Swan and 
                 Nadarajen Veerapen",
  title =        "Object-Oriented Genetic Improvement for Improved
                 Energy Consumption in {Google Guava}",
  booktitle =    "SSBSE",
  year =         "2015",
  editor =       "Yvan Labiche and Marcio Barros",
  volume =       "9275",
  series =       "LNCS",
  pages =        "255--261",
  address =      "Bergamo, Italy",
  month =        sep # " 5-7",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, Object-oriented programming,
                 Subclass substitution, Liskov Substitution Principle,
                 Energy profiling",
  isbn13 =       "978-3-319-22182-3",
  URL =          "https://dspace.stir.ac.uk/bitstream/1893/22227/1/SSBSE15-oogiiecgg.pdf",
  URL =          "http://hdl.handle.net/1893/22227",
  DOI =          "doi:10.1007/978-3-319-22183-0_20",
  size =         "6 pages",
  abstract =     "In this work we use metaheuristic search to improve
                 Google's Guava library, finding a semantically
                 equivalent version of
                 com.google.common.collect.ImmutableMultimap with
                 reduced energy consumption. Semantics-preserving
                 transformations are found in the source code, using the
                 principle of subtype polymorphism. We introduce a new
                 tool, Opacitor, to deterministically measure the energy
                 consumption, and find that a statistically significant
                 reduction to Guava's energy consumption is possible. We
                 corroborate these results using Jalen, and evaluate the
                 performance of the metaheuristic search compared to an
                 exhaustive search-finding that the same result is
                 achieved while requiring almost 200 times fewer fitness
                 evaluations. Finally, we compare the metaheuristic
                 search to an independent exhaustive search at each
                 variation point, finding that the metaheuristic has
                 superior performance.",
  notes =        "Excludes Java garbage collection and JIT. Exhaustive
                 search (80 CPU days). http://ssbse.org/2015",

Genetic Programming entries for Nathan Burles Edward Bowles Alexander E I Brownlee Zoltan Kocsis Jerry Swan Nadarajen Veerapen