- author = "Aymeric Blot and Justyna Petke",
- title = "Synthetic Benchmarks for Genetic Improvement",
- booktitle = "GI @ ICSE 2020",
- year = "2020",
- month = "3 " # jul,
- editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce",
- publisher = "ACM",
- address = "internet",
- pages = "287--288",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE",
- isbn13 = "978-1-4503-7963-2",
-
URL = "
http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-2.pdf",
-
video_url = "
https://youtu.be/GsNKCifm44A",
-
DOI = "
doi:10.1145/3387940.3392175",
- size = "2 pages",
- 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.",
-
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",
