Human Guidance Approaches for the Genetic Improvement of Software
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Craine:2024:GI,
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author = "Benjamin J. Craine and Penn {Faulkner Rainford} and
Barry Porter",
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title = "Human Guidance Approaches for the Genetic Improvement
of Software",
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booktitle = "13th International Workshop on Genetic Improvement
@ICSE 2024",
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year = "2024",
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editor = "Gabin An and Aymeric Blot and Vesna Nowack and
Oliver Krauss and Justyna Petke",
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pages = "21--22",
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address = "Lisbon",
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month = "16 " # apr,
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, source code, fitness plateau, neutral
search spaces, experienced human engineer",
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isbn13 = "979-8-4007-0573-1/24/04",
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URL = "http://gpbib.cs.ucl.ac.uk/gi2024/Craine_2024_GI.pdf",
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DOI = "doi:10.1145/3643692.3648263",
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slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/gi_icse2024_craine.pdf",
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video_url = "https://youtu.be/WvfuRLg_mJ0",
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video_url = "https://www.youtube.com/watch?v=WvfuRLg_mJ0&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=7&pp=iAQB",
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size = "2 pages",
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abstract = "Existing research on Genetic Improvement (GI) of
source code to improve performance
\cite{rainford:2022:GECCO} has examined the mixed
application of code synthesis and traditional GI
mutation/crossover to gain higher-performing
individuals that are tailored to particular deployment
contexts, for examples such as hash tables or
scheduling algorithms. While demonstrating successful
improvements, this research presents a host of
challenges \cite{Rainford:2021:GI}, from search space
size to fitness landscape shape, which raise questions
on whether GI alone is able to present a complete
solution. In this position paper we propose to augment
GI processes with Human Guidance (HG) to offer a
co-pilot paradigm which may overcome these
challenges.",
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notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}",
- }
Genetic Programming entries for
Benjamin J Craine
Penelope Faulkner Rainford
Barry Porter
Citations