Comparing the Expressive Power of Strongly-Typed and Grammar-Guided Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{fonseca:2023:GECCO,

• author = "Alcides Fonseca and Diogo Pocas",
• title = "Comparing the Expressive Power of {Strongly-Typed} and {Grammar-Guided} Genetic Programming",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• 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",
• pages = "1100--1108",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar-guided genetic programming, strongly-typed genetic programming",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590507",
• size = "9 pages",
• 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.",
• 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