Examining the "Best of Both Worlds" of Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Whigham:2015:GECCO,
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author = "Peter A. Whigham and Grant Dick and
James Maclaurin and Caitlin A. Owen",
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title = "Examining the {"}Best of Both Worlds{"} of Grammatical
Evolution",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "1111--1118",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754784",
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DOI = "doi:10.1145/2739480.2754784",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Grammatical Evolution (GE) has a long history in
evolutionary computation. Central to the behaviour of
GE is the use of a linear representation and grammar to
map individuals from search spaces into problem spaces.
This genotype to phenotype mapping is often argued as a
distinguishing property of GE relative to other
techniques, such as context-free grammar genetic
programming (CFG-GP). Since its initial description, GE
research has attempted to incorporate information from
the grammar into crossover, mutation, and individual
initialisation, blurring the distinction between
genotype and phenotype and creating GE variants closer
to CFG-GP. This is argued to provide GE with the best
of both worlds, allowing degrees of grammatical bias to
be introduced into operators to best suit the given
problem. This paper examines the behaviour of three
grammar-based search methods on several problems from
previous GE research. It is shown that, unlike CFG-GP,
the performance of pure GE on the examined problems
closely resembles that of random search. The results
suggest that further work is required to determine the
cases where the best of both worlds of GE are required
over a straight CFG-GP approach.",
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notes = "Also known as \cite{2754784} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Peter Alexander Whigham
Grant Dick
James Maclaurin
Caitlin A Owen
Citations