Iterative genetic improvement: Scaling stochastic program synthesis
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- @Article{DBLP:journals/ai/YuanB23,
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author = "Yuan Yuan and Wolfgang Banzhaf",
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title = "Iterative genetic improvement: Scaling stochastic
program synthesis",
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journal = "Artificial Intelligence",
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year = "2023",
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volume = "322",
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pages = "103962",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Genetic
improvement, Evolutionary computation, Program
synthesis, Artificial intelligence, AI",
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timestamp = "Sat, 05 Aug 2023 00:02:52 +0200",
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biburl = "https://dblp.org/rec/journals/ai/YuanB23.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "http://www.cs.mun.ca/~banzhaf/papers/IGI_AIJournal_2023.pdf",
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URL = "https://arxiv.org/abs/2202.13040",
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DOI = "doi:10.1016/J.ARTINT.2023.103962",
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size = "24 pages",
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abstract = "Program synthesis aims to automatically find programs
from an underlying programming language that satisfy a
given specification. While this has the potential to
revolutionize computing, how to search over the vast
space of programs efficiently is an unsolved challenge
in program synthesis. In cases where large programs are
required for a solution, it is generally believed that
stochastic search has advantages over other classes of
search techniques. Unfortunately, existing stochastic
program synthesizers do not meet this expectation very
well, suffering from the scalability issue. To overcome
this problem, we propose a new framework for stochastic
program synthesis, called iterative genetic
improvement. The key idea is to apply genetic
improvement to improve a current reference program, and
then iteratively replace the reference program by the
best program found. Compared to traditional stochastic
synthesis approaches, iterative genetic improvement can
build up the complexity of programs incrementally in a
more robust way. We evaluate the approach on two
program synthesis domains: list manipulation and string
transformation, along with a number of general program
synthesis problems. Our empirical results indicate that
this method has considerable advantages over several
representative stochastic program synthesizer
techniques, both in terms of scalability and of
solution quality.",
- }
Genetic Programming entries for
Yuan Yuan
Wolfgang Banzhaf
Citations