Using distributed genetic programming to evolve classifiers for a brain computer interface

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

@InProceedings{conf/esann/Alfaro-CidES06,

• title = "Using distributed genetic programming to evolve classifiers for a brain computer interface",
• author = "Eva Alfaro-Cid and Anna Esparcia-Alc{\'a}zar and Ken Sharman",
• year = "2006",
• booktitle = "ESANN'2006 proceedings - European Symposium on Artificial Neural Networks",
• editor = "Michel Verleysen",
• pages = "59--66",
• address = "Bruges, Belgium",
• month = "26-28 " # apr,
• bibdate = "2006-08-30",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/esann/esann2006.html#Alfaro-CidES06",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "2-930307-06-4",
• URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf",
• abstract = "The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high performance distributed genetic programming (GP) has been implemented for evolving classifiers. The particular application discussed herein is the classification of human electroencephalographic (EEG) signals for a brain-computer interface (BCI). The resulting classifying structures provide classification rates comparable to those obtained using traditional, human-designed, classification",
• notes = "http://www.dice.ucl.ac.be/Proceedings/esann/",

}

Genetic Programming entries for Eva Alfaro-Cid Anna Esparcia-Alcazar Kenneth C Sharman

Citations