Synthesis through Unification Genetic Programming
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- @InProceedings{Welsch:2020:GECCO,
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author = "Thomas Welsch and Vitaliy Kurlin",
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title = "Synthesis through Unification Genetic Programming",
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year = "2020",
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editor = "Carlos Artemio {Coello Coello} and
Arturo Hernandez Aguirre and Josu Ceberio Uribe and
Mario Garza Fabre and Gregorio {Toscano Pulido} and
Katya Rodriguez-Vazquez and Elizabeth Wanner and
Nadarajen Veerapen and Efren Mezura Montes and
Richard Allmendinger and Hugo Terashima Marin and
Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and
Heike Trautmann and Ke Tang and John Koza and
Erik Goodman and William B. Langdon and Miguel Nicolau and
Christine Zarges and Vanessa Volz and Tea Tusar and
Boris Naujoks and Peter A. N. Bosman and
Darrell Whitley and Christine Solnon and Marde Helbig and
Stephane Doncieux and Dennis G. Wilson and
Francisco {Fernandez de Vega} and Luis Paquete and
Francisco Chicano and Bing Xue and Jaume Bacardit and
Sanaz Mostaghim and Jonathan Fieldsend and
Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and
Carlos Segura and Carlos Cotta and Michael Emmerich and
Mengjie Zhang and Robin Purshouse and Tapabrata Ray and
Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and
Frank Neumann",
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isbn13 = "9781450371285",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377930.3390208",
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DOI = "doi:10.1145/3377930.3390208",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference",
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pages = "1029--1036",
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size = "8 pages",
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keywords = "genetic algorithms, genetic programming, STUN GP,
CDGP, grammatical evolution, divide and conquer, search
based program synthesis",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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abstract = "We present a new method, Synthesis through Unification
Genetic Programming (STUN GP), which synthesizes
provably correct programs using a Divide and Conquer
approach. This method first splits the input space by
undergoing a discovery phase that uses
Counterexample-Driven Genetic Programming (CDGP) to
identify a set of programs that are provably correct
under unknown unification constraints. The STUN GP
method then computes these restraints by synthesizing
predicates with CDGP that strictly map inputs to
programs where the output will be correct.
This method builds on previous work towards applying
Genetic Programming (GP) to Syntax Guided Synthesis
(SyGus) problems that seek to synthesize programs
adhering to a formal specification rather than a fixed
set of input-output examples. We show that our method
is more scalable than previous CDGP variants, solving
several benchmarks from the SyGus Competition that
cannot be solved by CDGP. STUN GP significantly cuts
into the gap between GP and state-of-the-art SyGus
solvers and further demonstrates Genetic Programming's
potential for Program Synthesis.",
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notes = "program synthesis, proof tool, SMTlib, microsoft Z3,
generates counter-examples. Grammar. max4( x y x
w)...max12() check-synth. CLIA. STUN GP
Also known as \cite{10.1145/3377930.3390208} GECCO-2020
A Recombination of the 29th International Conference on
Genetic Algorithms (ICGA) and the 25th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Thomas Welsch
Vitaliy Kurlin
Citations