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  5. Defining locality in genetic programming to predict performance
 
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Defining locality in genetic programming to predict performance

Author(s)
Galván-López, Edgar  
McDermott, James  
O'Neill, Michael  
Brabazon, Anthony  
Uri
http://hdl.handle.net/10197/2559
Date Issued
2010-07
Date Available
2010-11-18T16:37:10Z
Abstract
A key indicator of problem difficulty in evolutionary computation problems is the landscape’s locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype- fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2010 IEEE
Subjects

Genetic programming

Locality

Problem difficulty

Evolutionary computat...

Subject – LCSH
Genetic programming (Computer science)
Evolutionary computation
Genetic algorithms
DOI
10.1109/CEC.2010.5586095
Web versions
http://dx.doi.org/10.1109/CEC.2010.5586095
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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Defining Locality.pdf

Size

152.39 KB

Format

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Checksum (MD5)

a92e153b254958c54e0e9020845fa1ca

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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