Towards Objective-Tailored Genetic Improvement Through Large Language Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8097
- @InProceedings{Kang:2023:GI,
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author = "Sungmin Kang and Shin Yoo",
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title = "Towards Objective-Tailored Genetic Improvement Through
Large Language Models",
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booktitle = "12th International Workshop on Genetic Improvement
@ICSE 2023",
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year = "2023",
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editor = "Vesna Nowack and Markus Wagner and Gabin An and
Aymeric Blot and Justyna Petke",
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pages = "19--20",
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address = "Melbourne, Australia",
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month = "20 " # may,
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publisher = "IEEE",
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note = "{Best position paper}",
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, optimisation, AI, ANN, LLM, LLM+GI,
code-davinci-002 OpenAI, Python Fibonacci, execution
time, memory consumption",
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isbn13 = "979-8-3503-1232-4",
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URL = "https://arxiv.org/abs/2304.09386",
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URL = "http://gpbib.cs.ucl.ac.uk/gi2023/Kang_2023_GI.pdf",
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DOI = "doi:10.1109/GI59320.2023.00013",
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slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Kang_GI2023.pdf",
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video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Kang_GI2023.mp4",
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video_url = "https://www.youtube.com/watch?v=k3AkQ9liAJc&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=6",
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size = "2 pages",
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abstract = "While Genetic Improvement (GI) is a useful paradigm to
improve functional and nonfunctional aspects of
software, existing techniques tended to use the same
set of mutation operators for differing objectives, due
to the difficulty of writing custom mutation operators.
we suggest that Large Language Models (LLMs) can be
used to generate objective-tailored mutants, expanding
the possibilities of software optimisations that GI can
perform. We further argue that LLMs and the GI process
can benefit from the strengths of one another, and
present a simple example demonstrating that LLMs can
both improve the effectiveness of the GI optimization
process, while also benefiting from the evaluation
steps of GI. As a result, we believe that the
combination of LLMs and GI has the capability to
significantly aid developers in optimizing their
software.",
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notes = "Sungmin Kang also won the best presentation
award.
'one can ask an LLM to change code in a specific manner
using natural language'
GI @ ICSE 2023, part of \cite{Nowack:2023:GI}",
- }
Genetic Programming entries for
Sungmin Kang
Shin Yoo
Citations