short-paper

Genetic Improvement using Higher Order Mutation

Authors:

Mark Harman, and

Jens KrinkeAuthors Info & Claims

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

July 2015

Pages 803 - 804

https://doi.org/10.1145/2739482.2768417

Published: 11 July 2015 Publication History

Abstract

This paper presents a brief outline of a higher-order mutation-based framework for Genetic Improvement (GI). We argue that search-based higher-order mutation testing can be used to implement a form of genetic programming (GP) to increase the search granularity and testability of GI.

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Cited By

Wang XYu TArcaini PYue TAli SWagner M(2022)Mutation-based test generation for quantum programs with multi-objective searchProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528869(1345-1353)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528869
Smigielska MBlot APetke J(2021)Uniform Edit Selection for Genetic Improvement: Empirical Analysis of Mutation Operator Efficacy2021 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI52543.2021.00009(1-8)Online publication date: May-2021
https://doi.org/10.1109/GI52543.2021.00009
Prado Lima JVergilio S(2019)A systematic mapping study on higher order mutation testingJournal of Systems and Software10.1016/j.jss.2019.04.031154:C(92-109)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1016/j.jss.2019.04.031
Show More Cited By

Index Terms

Genetic Improvement using Higher Order Mutation
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Information & Contributors

Information

Published In

cover image ACM Conferences

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

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

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 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]

Sponsors

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: 11 July 2015

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Short-paper

Funding Sources

EPSRC

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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

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Citations

Cited By

Wang XYu TArcaini PYue TAli SWagner M(2022)Mutation-based test generation for quantum programs with multi-objective searchProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528869(1345-1353)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528869
Smigielska MBlot APetke J(2021)Uniform Edit Selection for Genetic Improvement: Empirical Analysis of Mutation Operator Efficacy2021 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI52543.2021.00009(1-8)Online publication date: May-2021
https://doi.org/10.1109/GI52543.2021.00009
Prado Lima JVergilio S(2019)A systematic mapping study on higher order mutation testingJournal of Systems and Software10.1016/j.jss.2019.04.031154:C(92-109)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1016/j.jss.2019.04.031
Basios MLi LWu FKanthan LBarr ELeavens GGarcia APăsăreanu C(2018)Darwinian data structure selectionProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236043(118-128)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236043
Petke JHaraldsson SHarman MLangdon WWhite DWoodward J(2018)Genetic Improvement of Software: A Comprehensive SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.269321922:3(415-432)Online publication date: Jun-2018
https://doi.org/10.1109/TEVC.2017.2693219
Petke J(2017)New operators for non-functional genetic improvementProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082520(1541-1542)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082520
Ghiduk AGirgis MShehata M(2017)Higher order mutation testing: A Systematic Literature ReviewComputer Science Review10.1016/j.cosrev.2017.06.00125(29-48)Online publication date: Aug-2017
https://doi.org/10.1016/j.cosrev.2017.06.001
Wu FHarman MJia YKrinke J(2016)HOMI: Searching Higher Order Mutants for Software ImprovementSearch Based Software Engineering10.1007/978-3-319-47106-8_2(18-33)Online publication date: 24-Sep-2016
https://doi.org/10.1007/978-3-319-47106-8_2

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