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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31579
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dc.contributor.authorPetke, Justynaen_UK
dc.contributor.authorAlexander, Braden_UK
dc.contributor.authorBarr, Earl Ten_UK
dc.contributor.authorBrownlee, Alexander E Ien_UK
dc.contributor.authorWagner, Markusen_UK
dc.contributor.authorWhite, David Ren_UK
dc.contributor.editorLópez-Ibáñez, Manuelen_UK
dc.date.accessioned2020-08-20T00:08:44Z-
dc.date.available2020-08-20T00:08:44Z-
dc.date.issued2019en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31579-
dc.description.abstractGenetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date.en_UK
dc.language.isoenen_UK
dc.publisherAssociation for Computing Machineryen_UK
dc.relationPetke J, Alexander B, Barr ET, Brownlee AEI, Wagner M & White DR (2019) A Survey of Genetic Improvement Search Spaces. In: López-Ibáñez M (ed.) GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO '19 - Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: Association for Computing Machinery, pp. 1715-1721. https://doi.org/10.1145/3319619.3326870en_UK
dc.rightsPermission 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 permissions@acm.org. GECCO ’19 Companion, July 13–17, 2019, Prague, Czech Republic © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.en_UK
dc.titleA Survey of Genetic Improvement Search Spacesen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1145/3319619.3326870en_UK
dc.citation.spage1715en_UK
dc.citation.epage1721en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.contributor.funderEPSRC Engineering and Physical Sciences Research Councilen_UK
dc.citation.btitleGECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companionen_UK
dc.citation.conferencedates2019-07-13 - 2019-07-17en_UK
dc.citation.conferencelocationPrague, Czech Republicen_UK
dc.citation.conferencenameGECCO '19 - Genetic and Evolutionary Computation Conferenceen_UK
dc.citation.isbn978-1-4503-6748-6en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationUniversity College Londonen_UK
dc.contributor.affiliationUniversity of Adelaideen_UK
dc.contributor.affiliationUniversity College Londonen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Adelaideen_UK
dc.contributor.affiliationUniversity of Sheffielden_UK
dc.identifier.isiWOS:000538328100299en_UK
dc.identifier.scopusid2-s2.0-85070602572en_UK
dc.identifier.wtid1648739en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.date.accepted2019-04-17en_UK
dcterms.dateAccepted2019-04-17en_UK
dc.date.filedepositdate2020-08-19en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relation.funderrefEP/J017515/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorPetke, Justyna|en_UK
local.rioxx.authorAlexander, Brad|en_UK
local.rioxx.authorBarr, Earl T|en_UK
local.rioxx.authorBrownlee, Alexander E I|0000-0003-2892-5059en_UK
local.rioxx.authorWagner, Markus|en_UK
local.rioxx.authorWhite, David R|en_UK
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.contributorLópez-Ibáñez, Manuel|en_UK
local.rioxx.freetoreaddate2020-08-19en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2020-08-19|en_UK
local.rioxx.filenamewksp187s2-file1.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-1-4503-6748-6en_UK
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