Using Genetic Programming to Estimate Performance of Computational Intelligence Models

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/icannga/SmidN13,

• author = "Jakub Smid and Roman Neruda",
• title = "Using Genetic Programming to Estimate Performance of Computational Intelligence Models",
• booktitle = "Proceedings 11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013",
• year = "2013",
• editor = "Marco Tomassini and Alberto Antonioni and Fabio Daolio and Pierre Buesser",
• volume = "7824",
• series = "Lecture Notes in Computer Science",
• pages = "169--178",
• address = "Lausanne, Switzerland",
• month = apr # " 4-6",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-05-27",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icannga/icannga2013.html#SmidN13",
• isbn13 = "978-3-642-37212-4",
• URL = "http://dx.doi.org/10.1007/978-3-642-37213-1",
• DOI = "doi:10.1007/978-3-642-37213-1_18",
• size = "10 pages",
• abstract = "This paper deals with the problem of choosing the most suitable model for a new data mining task. The metric is proposed on the data mining tasks space, and similar tasks are identified based on this metric. A function estimating models performance on the new task from both the time and error point of view is evolved by means of genetic programming. The approach is verified on data containing results of several hundred thousands machine learning experiments.",

}

Genetic Programming entries for Jakub Smid Roman Neruda

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