A Survey of Genetic Improvement Search Spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Petke:2019:GI7,
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author = "Justyna Petke and Brad Alexander and Earl T. Barr and
Alexander E. I. Brownlee and Markus Wagner and
David R. White",
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title = "A Survey of Genetic Improvement Search Spaces",
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booktitle = "7th edition of GI @ GECCO 2019",
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year = "2019",
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month = jul # " 13-17",
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editor = "Brad Alexander and Saemundur O. Haraldsson and
Markus Wagner and John R. Woodward",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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address = "Prague, Czech Republic",
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pages = "1715--1721",
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, Search-based Software Engineering, Program
Repair, Search Space, Fitness Landscape, GI, APR, SBSE,
NFR",
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isbn13 = "978-1-4503-6748-6",
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URL = "https://cs.adelaide.edu.au/~markus/pub/2019gecco-giSurvey.pdf",
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DOI = "doi:10.1145/3319619.3326870",
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size = "7 pages",
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abstract = "Genetic Improvement (GI) uses automated search to
improve existing software. Most GI work has focused on
empirical studies that successfully apply GI to improve
softwares 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
GIs mutation search space. This paper summarises the
work published on this question to date",
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notes = "
Also known as \cite{Petke:2019:GECCOcomp}
Also known as \cite{3326870} GECCO-2019 A Recombination
of the 28th International Conference on Genetic
Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Justyna Petke
Brad Alexander
Earl Barr
Alexander E I Brownlee
Markus Wagner
David Robert White
Citations