Genetic Programming Using a Minimum Description Length Principle
Created by W.Langdon from
gp-bibliography.bib Revision:1.6712
- @InCollection{kinnear:iba,
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title = "Genetic Programming Using a Minimum Description Length
Principle",
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author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
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booktitle = "Advances in Genetic Programming",
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publisher = "MIT Press",
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editor = "Kenneth E. {Kinnear, Jr.}",
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year = "1994",
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pages = "265--284",
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chapter = "12",
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size = "15 pages",
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URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-15.pdf",
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URL = "
http://citeseer.ist.psu.edu/327857.html",
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URL = "
http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
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URL = "
http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap12.pdf",
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keywords = "genetic algorithms, genetic programming",
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abstract = "This paper introduces a Minimum Description Length
(MDL) principle to define fitness functions in Genetic
Programming (GP). In traditional (Koza-style) GP, the
size of trees was usually controlled by user-defined
parameters, such as the maximum number of nodes and
maximum tree depth. Large tree sizes meant that the
time necessary to measure their fitnesses often
dominated total processing time. To overcome this
difficulty, we introduce a method for controlling tree
growth, which uses an MDL principle. Initially we
choose a {"}decision tree{"} representation for the GP
chromosomes, and then show how an MDL principle can be
used to define GP fitness functions. Thereafter we
apply the MDL-based fitness functions to some practical
problems. Using our implemented system
{"}STROGANOFF{"}, we show how MDL-based fitness
functions can be applied successfully to problems of
pattern recognitions. The results demonstrate that our
approach is superior to usual neural networks in terms
of general...",
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notes = "Describes MDL; Work on both decision trees and GMDH
symbolic regression trees (STROGANOFF). Nature of trees
(ie never worse than component trees) more important
than MDL?
",
- }
Genetic Programming entries for
Hitoshi Iba
Hugo de Garis
Taisuke Sato
Citations