Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions

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@InProceedings{Garciarena:2016:GI,
  author =       "Unai Garciarena and Roberto Santana",
  title =        "Evolutionary optimization of compiler flag selection
                 by learning and exploiting flags interactions",
  booktitle =    "Genetic Improvement 2016 Workshop",
  year =         "2016",
  editor =       "Justyna Petke and David R. White and Westley Weimer",
  pages =        "1159--1166",
  address =      "Denver",
  publisher_address = "New York, NY, USA",
  month =        jul # " 20-24",
  organisation = "SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, compiler flag selection, compiler
                 optimization, probabilistic modeling, EDAs",
  URL =          "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Evolutionary_optimization_of_compiler_flag_selection_by_learning_and_exploiting_flags_interactions.pdf",
  DOI =          "doi:10.1145/2908961.2931696",
  size =         "8 pages",
  abstract =     "Compiler flag selection can be an effective way to
                 increase the quality of executable code according to
                 different code quality criteria. Evolutionary
                 algorithms have been successfully applied to this
                 optimization problem. However, previous approaches have
                 only partially addressed the question of capturing and
                 exploiting the interactions between compilation options
                 to improve the search. In this paper we deal with this
                 question comparing estimation of distribution
                 algorithms (EDAs) and a traditional genetic algorithm
                 approach. We show that EDAs that learn bivariate
                 interactions can improve the results of GAs for some of
                 the programs considered. We also show that the
                 probabilistic models generated as a result of the
                 search for optimal flag combinations can be used to
                 unveil the (problem-dependent) interactions between the
                 flags, allowing the user a more informed choice of
                 compilation options.",
  notes =        "GECCO 2016 Workshop
                 http://geneticimprovementofsoftware.com/",
}

Genetic Programming entries for Unai Garciarena Hualde Roberto Santana

Citations