Reducing Energy Consumption Using Genetic Improvement

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{bruce2015reducing,

• author = "Bobby R. Bruce and Justyna Petke and Mark Harman",
• title = "Reducing Energy Consumption Using Genetic Improvement",
• 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",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Genetic Improvement, GI, SBSE, Search-Based Software Engineering and Self-* Search, Software Engineering, optimisation, energy optimisation, energy efficiency, energy consumption, Boolean satisfiability, SAT",
• URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2015_GECCO.pdf",
• URL = "http://doi.acm.org/10.1145/2739480.2754752",
• DOI = "doi:10.1145/2739480.2754752",
• size = "8 pages",
• 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

Citations