research-article

Multi-objective optimization of dynamic memory managers using grammatical evolution

Authors:

J. Manuel Colmenar,

José L. Risco-Martín,

J. Ignacio HidalgoAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1819 - 1826

https://doi.org/10.1145/2001576.2001820

Published: 12 July 2011 Publication History

Abstract

The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimization methodology applied to DMM optimization where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimization run. Our results show that the multi-objective optimization provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.

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Cited By

Bera AKlesk PSychel D(2019)Constant-Time Calculation of Zernike Moments for Detection with Rotational InvarianceIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.280382841:3(537-551)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TPAMI.2018.2803828
Yang XMei HZhang JXu KYin BZhang QWei X(2019)DRFNIEEE Transactions on Multimedia10.1109/TMM.2018.286360221:2(328-337)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TMM.2018.2863602
Pagan JFallahzadeh RPedram MRisco-Martin JMoya JAyala JGhasemzadeh H(2019)Toward Ultra-Low-Power Remote Health MonitoringIEEE Transactions on Mobile Computing10.1109/TMC.2018.284337318:3(658-673)Online publication date: 1-Mar-2019
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Index Terms

Multi-objective optimization of dynamic memory managers using grammatical evolution
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Cited By

Bera AKlesk PSychel D(2019)Constant-Time Calculation of Zernike Moments for Detection with Rotational InvarianceIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.280382841:3(537-551)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TPAMI.2018.2803828
Yang XMei HZhang JXu KYin BZhang QWei X(2019)DRFNIEEE Transactions on Multimedia10.1109/TMM.2018.286360221:2(328-337)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TMM.2018.2863602
Pagan JFallahzadeh RPedram MRisco-Martin JMoya JAyala JGhasemzadeh H(2019)Toward Ultra-Low-Power Remote Health MonitoringIEEE Transactions on Mobile Computing10.1109/TMC.2018.284337318:3(658-673)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TMC.2018.2843373
Pagán JFallahzadeh RGhasemzadeh HMoya JRisco-Martín JAyala J(2017)An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devicesProceedings of the Conference on Design, Automation & Test in Europe10.5555/3130379.3130685(1297-1302)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.5555/3130379.3130685
Pagan JFallahzadeh RGhasemzadeh HMoya JRisco-Martin JAyala J(2017)An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devicesDesign, Automation & Test in Europe Conference & Exhibition (DATE), 201710.23919/DATE.2017.7927193(1297-1302)Online publication date: Mar-2017
https://doi.org/10.23919/DATE.2017.7927193
Wu FWeimer WHarman MJia YKrinke JEsparcia-Alcázar A(2015)Deep Parameter OptimisationProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754648(1375-1382)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754648
Langdon W(2015)Genetically Improved SoftwareHandbook of Genetic Programming Applications10.1007/978-3-319-20883-1_8(181-220)Online publication date: 2015
https://doi.org/10.1007/978-3-319-20883-1_8

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