Treating Noisy Data Sets with Relaxed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Treating Noisy Data Sets with Relaxed Genetic
  author =       "Luis E. {Da Costa} and Jacques-Andre Landry and 
                 Yan Levasseur",
  year =         "2007",
  volume =       "4926",
  bibdate =      "2008-05-16",
  bibsource =    "DBLP,
  booktitle =    "Artificial Evolution",
  editor =       "Nicolas Monmarch{\'e} and El-Ghazali Talbi and 
                 Pierre Collet and Marc Schoenauer and Evelyne Lutton",
  isbn13 =       "978-3-540-79304-5",
  pages =        "1--12",
  series =       "Lecture Notes in Computer Science",
  address =      "Tours, France",
  month =        "31-29 " # oct,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-540-79305-2_1",
  abstract =     "In earlier papers we presented a technique (RelaxGP)
                 for improving the performance of the solutions
                 generated by Genetic Programming (GP) applied to
                 regression and approximation of symbolic functions.
                 RelaxGP changes the definition of a perfect solution:
                 in standard symbolic regression, a perfect solution
                 provides exact values for each point in the training
                 set. RelaxGP allows a perfect solution to belong to a
                 certain interval around the desired values.

                 We applied RelaxGP to regression problems where the
                 input data is noisy. This is indeed the case in several
                 real-world problems, where the noise comes, for
                 example, from the imperfection of sensors. We compare
                 the performance of solutions generated by GP and by
                 RelaxGP in the regression of 5 noisy sets. We show that
                 RelaxGP with relaxation values of 10percent to
                 100percent of the Gaussian noise found in the data can
                 outperform standard GP, both in terms of generalization
                 error reached and in resources required to reach a
                 given test error.",
  notes =        "EA'07",

Genetic Programming entries for Luis E Da Costa Jacques-Andre Landry Yan Levasseur