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  5. Higher-order functions in aesthetic EC encodings
 
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Higher-order functions in aesthetic EC encodings

Author(s)
McDermott, James  
Byrne, Jonathan  
Swafford, John Mark  
O'Neill, Michael  
Brabazon, Anthony  
Uri
http://hdl.handle.net/10197/2554
Date Issued
2010-07
Date Available
2010-11-15T15:03:22Z
Abstract
The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order functions, and describe their general advantages in EC encodings. We implement grammars using higher-order ideas for two problem domains, music and 3D architectural design, and use these grammars in the grammatical evolution paradigm. We demonstrate four advantages of higher-order functions (patterning of phenotypes, non-entropic mutations, compression of genotypes, and natural expression of artistic knowledge) which lead to beneficial results on our problems.
Sponsorship
Science Foundation Ireland
Irish Research Council for Science, Engineering and Technology
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2010 IEEE
Subjects

Higher-order function...

Design

Grammatical evolution...

Genetic programming

Evolutionary computat...

Natural computing

Subject – LCSH
Functional programming (Computer science)
Evolutionary computation
Genetic programming (Computer science)
Natural computation
DOI
10.1109/CEC.2010.5586077
Web versions
http://dx.doi.org/10.1109/CEC.2010.5586077
Language
English
Status of Item
Peer reviewed
Journal
Evolutionary Computation (CEC), 2010 IEEE Congress on [proceedings]
Conference Details
IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation, Barcelona, Spain, 18-23 July
ISSN
978-1-4244-6909-3
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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High Order.pdf

Size

937.45 KB

Format

Adobe PDF

Checksum (MD5)

5fd29207180634a02a28086e42a2cdfc

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/.
All other content is subject to copyright.

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