Comparing the Expressive Power of Strongly-Typed and Grammar-Guided Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{fonseca:2023:GECCO,
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author = "Alcides Fonseca and Diogo Pocas",
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title = "Comparing the Expressive Power of {Strongly-Typed} and
{Grammar-Guided} Genetic Programming",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "1100--1108",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, grammar-guided genetic programming,
strongly-typed genetic programming",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590507",
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size = "9 pages",
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abstract = "Since Genetic Programming (GP) has been proposed,
several flavors of GP have arisen, each with their own
strengths and limitations. Grammar-Guided and
Strongly-Typed GP (GGGP and STGP, respectively) are two
popular flavors that have the advantage of allowing the
practitioner to impose syntactic and semantic
restrictions on the generated programs. GGGP makes use
of (traditionally context-free) grammars to restrict
the generation of (and the application of genetic
operators on) individuals. By guiding this generation
according to a grammar, i.e. a set of rules, GGGP
improves performance by searching for an good-enough
solution on a subset of the search space. This approach
has been extended with Attribute Grammars to encode
semantic restrictions, while Context-Free Grammars
would only encode syntactic restrictions. STGP is also
able to restrict the shape of the generated programs
using a very simple grammar together with a type
system. In this work, we address the question of which
approach has more expressive power. We demonstrate that
STGP has higher expressive power than Context-Free GGGP
and less expressive power than Attribute Grammatical
Evolution.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Alcides Fonseca
Diogo Pocas
Citations