Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/ppsn/SikulovaS12,

• author = "Michaela Sikulova and Lukas Sekanina",
• title = "Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP",
• booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)",
• year = "2012",
• editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone",
• volume = "7491",
• series = "Lecture Notes in Computer Science",
• pages = "163--172",
• address = "Taormina, Italy",
• month = sep # " 1-5",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• isbn13 = "978-3-642-32936-4",
• DOI = "doi:10.1007/978-3-642-32937-1_17",
• size = "10 pages",
• 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.",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• 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