A Comparison of Evolvable Hardware Architectures for Classification Tasks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{gl-to-08,
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author = "Kyrre Glette and Jim Torresen and Paul Kaufmann and
Marco Platzner",
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title = "A Comparison of Evolvable Hardware Architectures for
Classification Tasks",
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booktitle = "8th International Conference on Evolvable Systems:
From Biology to Hardware: ICES 2008",
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year = "2008",
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editor = "Gregory S. Hornby and Lukas Sekanina and
Pauline C. Haddow",
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volume = "5216",
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series = "LNCS",
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pages = "22--33",
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address = "Prague, Czech Republic",
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month = sep # " 21-24",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-540-85857-7",
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DOI = "doi:10.1007/978-3-540-85857-7_3",
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size = "12 pages",
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abstract = "We analyse and compare four different evolvable
hardware approaches for classification tasks: An
approach based on a programmable logic array
architecture, an approach based on two-phase
incremental evolution, a generic logic architecture
with automatic definition of building blocks, and a
specialized coarse-grained architecture with
pre-defined building blocks. We base the comparison on
a common data set and report on classification accuracy
and training effort. The results show that
classification accuracy can be increased by using
modular, specialized classifier architectures.
Furthermore, function level evolution, either with
predefined functions derived from domain-specific
knowledge or with functions that are automatically
defined during evolution, also gives higher accuracy.
Incremental and function level evolution reduce the
search space and thus shortens the training effort.",
- }
Genetic Programming entries for
Kyrre Harald Glette
Jim Torresen
Paul Kaufmann
Marco Platzner
Citations