Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{conf/ppsn/SikulovaS12,
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author = "Michaela Sikulova and Lukas Sekanina",
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title = "Acceleration of Evolutionary Image Filter Design Using
Coevolution in Cartesian GP",
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booktitle = "Parallel Problem Solving from Nature, PPSN XII (part
1)",
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year = "2012",
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editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and
Kalyanmoy Deb and Stephanie Forrest and
Giuseppe Nicosia and Mario Pavone",
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volume = "7491",
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series = "Lecture Notes in Computer Science",
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pages = "163--172",
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address = "Taormina, Italy",
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month = sep # " 1-5",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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isbn13 = "978-3-642-32936-4",
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DOI = "doi:10.1007/978-3-642-32937-1_17",
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size = "10 pages",
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abstract = "The aim of this work is to accelerate the task of
evolutionary image filter design using coevolution of
candidate filters and training vectors subsets. Two
coevolutionary methods are implemented and compared for
this task in the framework of Cartesian Genetic
Programming (CGP). Experimental results show that only
15--20percent of original training vectors are needed
to find an image filter which provides the same quality
of filtering as the best filter evolved using the
standard CGP which uses the whole training set.
Moreover, the median time of evolution was reduced 2.99
times in comparison with the standard CGP.",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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affiliation = "Faculty of Information Technology, IT4Innovations
Centre of Excellence, Brno University of Technology,
Bozetechova 2, 612 66 Brno, Czech Republic",
- }
Genetic Programming entries for
Michaela Sikulova
Lukas Sekanina
Citations