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Tree-adjunct grammatical evolution

Author(s)
Murphy, Eoin  
O'Neill, Michael  
Galván-López, Edgar  
Brabazon, Anthony  
Uri
http://hdl.handle.net/10197/2558
Date Issued
2010-07
Date Available
2010-11-18T16:28:22Z
Abstract
In this paper we investigate the application of Tree-Adjunct Grammars to Grammatical Evolution. The standard type of grammar used by grammatical evolution, context-free grammars, produce a subset of the languages that tree-adjunct grammars can produce, making tree-adjunct grammars, expressively, more powerful. In this study we shed some light on the effects of tree-adjunct grammars in grammatical evolution, or Tree-Adjunct Grammatical Evolution. We perform an analytic comparison of the performance of both setups, i.e., grammatical evolution and tree-adjunct grammatical evolution, across a number of classic genetic programming benchmarking problems. The results firmly indicate that tree-adjunct grammatical evolution has a better overall performance (measured in terms of finding the global optima).
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2010 IEEE
Subjects

Grammatical evolution...

Tree adjunct grammar

Genetic programming

Evolutionary computat...

Subject – LCSH
Genetic programming (Computer science)
Evolutionary computation
DOI
10.1109/CEC.2010.5586497
Web versions
http://dx.doi.org/10.1109/CEC.2010.5586497
Language
English
Status of Item
Peer reviewed
Journal
2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]
Conference Details
Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
ISBN
978-1-4244-6909-3
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
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tree-adjunct.pdf

Size

659.92 KB

Format

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Checksum (MD5)

4f3e3a403b8ca6eddbd2f2f2003513c0

Owning collection
Computer Science Research Collection
Mapped collections
CASL Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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