Genetic improvement of software efficiency: the curse of fitness estimation

Mahmoud A. Bokhari; Markus Wagner; Brad Alexander

doi:10.1145/3377929.3398109

Genetic improvement of software efficiency: the curse of fitness estimation

Mahmoud A. Bokhari
, Markus Wagner
, Brad Alexander

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

3 Citations (Scopus)

Abstract

Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straightforward task. In this paper, we illustrate some of the challenges in computing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.

Original language	English
Title of host publication	Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Editors	Carlos A. Coello Coello
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1926-1927
Number of pages	2
ISBN (Electronic)	9781450371278
DOIs	https://doi.org/10.1145/3377929.3398109
Publication status	Published - 8 Jul 2020
Externally published	Yes
Event	The Genetic and Evolutionary Computation Conference 2020 - Cancun, Mexico Duration: 8 Jul 2020 → 12 Jul 2020 Conference number: 22nd https://gecco-2020.sigevo.org/index.html/HomePage https://dl.acm.org/doi/proceedings/10.1145/3377930 (Proceedings)

Conference

Conference	The Genetic and Evolutionary Computation Conference 2020
Abbreviated title	GECCO 2020
Country/Territory	Mexico
City	Cancun
Period	8/07/20 → 12/07/20
Internet address	https://gecco-2020.sigevo.org/index.html/HomePage https://dl.acm.org/doi/proceedings/10.1145/3377930 (Proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Android
Energy consumption
Genetic improvement
Machine learning
Mobile applications
Non-functional properties

Access to Document

10.1145/3377929.3398109

Cite this

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title = "Genetic improvement of software efficiency: the curse of fitness estimation",

abstract = "Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straightforward task. In this paper, we illustrate some of the challenges in computing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.",

keywords = "Android, Energy consumption, Genetic improvement, Machine learning, Mobile applications, Non-functional properties",

author = "Bokhari, \{Mahmoud A.\} and Markus Wagner and Brad Alexander",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; The Genetic and Evolutionary Computation Conference 2020, GECCO 2020 ; Conference date: 08-07-2020 Through 12-07-2020",

year = "2020",

month = jul,

day = "8",

doi = "10.1145/3377929.3398109",

language = "English",

pages = "1926--1927",

editor = "\{A. Coello Coello\}, Carlos",

booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://gecco-2020.sigevo.org/index.html/HomePage, https://dl.acm.org/doi/proceedings/10.1145/3377930",

}

Bokhari, MA, Wagner, M & Alexander, B 2020, Genetic improvement of software efficiency: the curse of fitness estimation. in C A. Coello Coello (ed.), Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM), New York NY USA, pp. 1926-1927, The Genetic and Evolutionary Computation Conference 2020, Cancun, Mexico, 8/07/20. https://doi.org/10.1145/3377929.3398109

Genetic improvement of software efficiency: the curse of fitness estimation. / Bokhari, Mahmoud A.; Wagner, Markus; Alexander, Brad.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. ed. / Carlos A. Coello Coello. New York NY USA: Association for Computing Machinery (ACM), 2020. p. 1926-1927.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Genetic improvement of software efficiency

T2 - The Genetic and Evolutionary Computation Conference 2020

AU - Bokhari, Mahmoud A.

AU - Wagner, Markus

AU - Alexander, Brad

N1 - Conference code: 22nd

PY - 2020/7/8

Y1 - 2020/7/8

N2 - Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straightforward task. In this paper, we illustrate some of the challenges in computing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.

AB - Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straightforward task. In this paper, we illustrate some of the challenges in computing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.

KW - Android

KW - Energy consumption

KW - Genetic improvement

KW - Machine learning

KW - Mobile applications

KW - Non-functional properties

UR - https://www.scopus.com/pages/publications/85089740688

U2 - 10.1145/3377929.3398109

DO - 10.1145/3377929.3398109

M3 - Conference Paper

AN - SCOPUS:85089740688

SP - 1926

EP - 1927

BT - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

A2 - A. Coello Coello, Carlos

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

Y2 - 8 July 2020 through 12 July 2020

ER -

Genetic improvement of software efficiency: the curse of fitness estimation

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this