On domain knowledge and novelty to improve program synthesis performance with grammatical evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Hemberg:2019:GECCO,
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author = "Erik Hemberg and Jonathan Kelly and Una-May O'Reilly",
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title = "On domain knowledge and novelty to improve program
synthesis performance with grammatical evolution",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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pages = "1039--1046",
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address = "Prague, Czech Republic",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, Multi-agent systems, grammar, program
synthesis, novelty",
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isbn13 = "978-1-4503-6111-8",
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URL = "https://alfagroup.csail.mit.edu/sites/default/files/documents/2019Domain_Knowledge_and_Novelty_to_Improve_Program_Synthesis_Performance_with_Grammatical_Evolution.pdf",
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DOI = "doi:10.1145/3321707.3321865",
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size = "8 pages",
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abstract = "Programmers solve coding problems with the support of
both programming and problem specific knowledge. They
integrate this domain knowledge to reason by
computational abstraction. Correct and readable code
arises from sound abstractions and problem solving. We
attempt to transfer insights from such human expertise
to genetic programming (GP) for solving automatic
program synthesis. We draw upon manual and non-GP
Artificial Intelligence methods to extract knowledge
from synthesis problem definitions to guide the
construction of the grammar that Grammatical Evolution
uses and to supplement its fitness function. We examine
the impact of using such knowledge on 21 problems from
the GP program synthesis benchmark suite. Additionally,
we investigate the compounding impact of this knowledge
and novelty search. The resulting approaches exhibit
improvements in accuracy on a majority of problems in
the fields benchmark suite of program synthesis
problems.",
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notes = "Also known as \cite{3321865} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Erik Hemberg
Jonathan Kelly
Una-May O'Reilly
Citations