Using unrestricted loops in genetic programming for image classification
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gp-bibliography.bib Revision:1.7975
- @InProceedings{Larres:2010:cec,
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author = "Jan Larres and Mengjie Zhang and Will N Browne",
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title = "Using unrestricted loops in genetic programming for
image classification",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-6910-9",
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abstract = "Loops are an important part of classic programming
techniques, but are rarely used in genetic programming.
This paper presents a method of using unrestricted,
i.e. nesting, loops to evolve programs for image
classification tasks. Contrary to many other
classification methods where pre-extracted features are
typically used, we perform calculations on image
regions determined by the loops. Since the loops can be
nested, these regions may depend on previously computed
regions, thereby allowing a simple version of
conditional evaluation. The proposed GP approach with
unrestricted loops is examined and compared with the
canonical GP method without loops and the GP approach
with restricted loops on one synthesised character
recognition problem and two texture classification
problems. The results suggest that unrestricted loops
can have an advantage over the other two methods in
certain situations for image classification.",
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DOI = "doi:10.1109/CEC.2010.5586305",
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notes = "WCCI 2010. Also known as \cite{5586305}",
- }
Genetic Programming entries for
Jan Larres
Mengjie Zhang
Will N Browne
Citations