Reducing Energy Consumption Using Genetic Improvement

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

  author =       "Bobby R. Bruce and Justyna Petke and Mark Harman",
  title =        "Reducing Energy Consumption Using Genetic
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and 
                 Terrence Soule and Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and 
                 Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and 
                 Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Keswsentini and Gabriela Ochoa and 
                 Francisco Chicano and Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1327--1334",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, Search-Based Software Engineering
                 and Self-* Search",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754752",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic Improvement (GI) is an area of Search Based
                 Software Engineering which seeks to improve software's
                 non-functional properties by treating program code as
                 if it were genetic material which is then evolved to
                 produce more optimal solutions. Hitherto, the majority
                 of focus has been on optimising program's execution
                 time which, though important, is only one of many
                 non-functional targets. The growth in mobile computing,
                 cloud computing infrastructure, and ecological concerns
                 are forcing developers to focus on the energy their
                 software consumes. We report on investigations into
                 using GI to automatically find more energy efficient
                 versions of the MiniSAT Boolean satisfiability solver
                 when specialising for three downstream applications.
                 Our results find that GI can successfully be used to
                 reduce energy consumption by up to 25percent",
  notes =        "Also known as \cite{Bruce:2015:GECCO} \cite{2754752}
                 GECCO-2015 A joint meeting of the twenty fourth
                 international conference on genetic algorithms
                 (ICGA-2015) and the twentith annual genetic programming
                 conference (GP-2015)",

Genetic Programming entries for Bobby R Bruce Justyna Petke Mark Harman