Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Aguilar:DEU:cec2006,
  author =       "Jose Aguilar and Gilberto Gonzalez",
  title =        "Data Extrapolation Using Genetic Programming to
                 Matrices Singular Values Estimation",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Simon M. Lucas and Gary Fogel and 
                 Graham Kendall and Ralf Salomon and 
                 Byoung-Tak Zhang and Carlos A. Coello Coello and 
                 Thomas Philip Runarsson",
  pages =        "3227--3230",
  address =      "Vancouver, BC, Canada",
  month =        "16-21 " # jul,
  publisher =    "IEEE Press",
  ISBN =         "0-7803-9487-9",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=11108",
  DOI =          "doi:10.1109/CEC.2006.1688718",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In mathematical models where the dimensions of the
                 matrices are very large, the use of classical methods
                 to compute the singular values is very time consuming
                 and requires a lot of computational resources. In this
                 way, it is necessary to find new faster methods to
                 compute the singular values of a very large matrix. We
                 present a method to estimate the singular values of a
                 matrix based on Genetic Programming (GP). GP is an
                 approach based on the evolutionary principles of the
                 species. GP is used to make extrapolations of data out
                 of sample data. The extrapolations of data are achieved
                 by irregularity functions which approximate very well
                 the trend of the sample data. GP produces from just
                 simple's functions, operators and a fitness function,
                 complex mathematical expressions that adjust smoothly
                 to a group of points of the form (xi, yi). We obtain
                 amazing mathematical formulas that follow the behaviour
                 of the sample data. We compare our algorithm with two
                 techniques: the linear regression and non linear
                 regression approaches. Our results suggest that we can
                 predict with some percentage of error the largest
                 singular values of a matrix without computing the
                 singular values of the whole matrix and using only some
                 random selected columns of the matrix.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Jose Lisandro Aguilar Castro Gilberto Gonzalez

Citations