Synthetic Benchmarks for Genetic Improvement
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Blot:2020:GI,
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author = "Aymeric Blot and Justyna Petke",
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title = "Synthetic Benchmarks for Genetic Improvement",
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booktitle = "GI @ ICSE 2020",
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year = "2020",
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month = "3 " # jul,
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editor = "Shin Yoo and Justyna Petke and Westley Weimer and
Bobby R. Bruce",
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publisher = "ACM",
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address = "internet",
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pages = "287--288",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, SBSE",
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isbn13 = "978-1-4503-7963-2",
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URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-2.pdf",
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video_url = "https://youtu.be/GsNKCifm44A",
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DOI = "doi:10.1145/3387940.3392175",
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size = "2 pages",
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abstract = "Genetic improvement (GI) uses automated search to find
improved versions of existing software. If over the
years the potential of many GI approaches have been
demonstrated, the intrinsic cost of evaluating
real-world software makes comparing these approaches in
large-scale meta-analyses very expensive. We propose
and describe a method to construct synthetic GI
benchmarks, to circumvent this bottleneck and enable
much faster quality assessment of GI approaches.",
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notes = "fitness is single query of surogate model using: AST,
run-time, testing. Model based on many mutations of the
original program Video: https://youtu.be/GsNKCifm44A
(start 2:12:38, 2:22:36 end 2:28:15)
Slides:
http://geneticimprovementofsoftware.com/slides/gi2020icse/synthetic_benchmarks_slides.pdf
http://geneticimprovementofsoftware.com/gi2020icse.html",
- }
Genetic Programming entries for
Aymeric Blot
Justyna Petke
Citations