Application of Cellular Genetic Programming in Data Mining

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

@InProceedings{takac:2004:KN,

• author = "Aleksandra Takac",
• title = "Application of Cellular Genetic Programming in Data Mining",
• booktitle = "Znalosti",
• year = "2004",
• editor = "Vaclav Snasel and Michal Kratky",
• address = "Brno, Czech Republic",
• month = "25-27 " # feb,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.ii.fmph.uniba.sk/~takaca/KN04.PDF",
• size = "12 pages",
• abstract = "Paper examines application of genetic programming framework in the problem of knowledge discovery in databases, more precisely in the task of classification. Genetic programming possesses certain advantages that make it suitable for application in data mining, such as robustness of algorithm or its convenient structure for rule generation to name a few. This study focuses on one type of parallel genetic algorithms ? cellular (diffusion) model. Emphasis is placed on the improvement of efficiency and scalability of data mining algorithm, which could be achieved by integration of algorithm with databases and by employing a cellular framework, as well as examining parallel approaches. Cellular model of genetic programming that exploits SQL queries is implemented and applied to classification task. Achieved results are compared with other machine learning algorithms.",
• notes = "http://www.fi.muni.cz/znalosti2004/profil_en.html.iso-8859-1",

}

Genetic Programming entries for Aleksandra Takac

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