research-article

Reducing Energy Consumption Using Genetic Improvement

Authors:

Bobby R. Bruce,

Mark HarmanAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1327 - 1334

https://doi.org/10.1145/2739480.2754752

Published: 11 July 2015 Publication History

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 25%

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Cited By

Rani PZellweger JKousadianos VCruz LKehrer TBacchelli ARoychoudhury PPaiva AAbreu RStorey MSharif BXia X(2024)Energy Patterns for Web: An Exploratory StudyProceedings of the 46th International Conference on Software Engineering: Software Engineering in Society10.1145/3639475.3640110(12-22)Online publication date: 14-Apr-2024
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Aragón-Jurado Jde la Torre JRuiz PGalindo PZomaya ADorronsoro B(2024)Automatic Software Tailoring for Optimal PerformanceIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33306719:3(464-481)Online publication date: May-2024
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Index Terms

Reducing Energy Consumption Using Genetic Improvement
1. Software and its engineering

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 11 July 2015

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Rani PZellweger JKousadianos VCruz LKehrer TBacchelli ARoychoudhury PPaiva AAbreu RStorey MSharif BXia X(2024)Energy Patterns for Web: An Exploratory StudyProceedings of the 46th International Conference on Software Engineering: Software Engineering in Society10.1145/3639475.3640110(12-22)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639475.3640110
Aragón-Jurado Jde la Torre JRuiz PGalindo PZomaya ADorronsoro B(2024)Automatic Software Tailoring for Optimal PerformanceIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33306719:3(464-481)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3330671
Dorronsoro BAragón-Jurado JJareño Jde la Torre JRuiz P(2024)A Survey on Automatic Source Code Transformation for Green Software GenerationEncyclopedia of Sustainable Technologies10.1016/B978-0-323-90386-8.00122-4(765-779)Online publication date: 2024
https://doi.org/10.1016/B978-0-323-90386-8.00122-4
Callan JPetke J(2024)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10515-024-00472-7
Fredericks EMoore JDiller A(2024)GenerativeGI: creating generative art with genetic improvementAutomated Software Engineering10.1007/s10515-024-00414-331:1Online publication date: 1-Mar-2024
https://doi.org/10.1007/s10515-024-00414-3
Biau JCussat-Blanc SLuga H(2024)Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing EfficiencyApplications of Evolutionary Computation10.1007/978-3-031-56852-7_2(19-34)Online publication date: 3-Mar-2024
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Li XChen JLiu YWu KGallagher J(2023)Combatting Energy Issues for Mobile ApplicationsACM Transactions on Software Engineering and Methodology10.1145/352785132:1(1-44)Online publication date: 13-Feb-2023
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Wedyan FMorrison RAbuomar O(2023)Integration and Unit Testing of Software Energy Consumption2023 Tenth International Conference on Software Defined Systems (SDS)10.1109/SDS59856.2023.10329262(60-64)Online publication date: 23-Oct-2023
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Weber MKaltenecker CSattler FApel SSiegmund N(2023)Twins or False Friends? A Study on Energy Consumption and Performance of Configurable Software2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00177(2098-2110)Online publication date: May-2023
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Kang SYoo S(2023)Towards Objective-Tailored Genetic Improvement Through Large Language Models2023 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI59320.2023.00013(19-20)Online publication date: May-2023
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