Interval-valued GA-P algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Luciano Sanchez",
  title =        "Interval-valued GA-P algorithms",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2000",
  volume =       "4",
  number =       "1",
  pages =        "64--72",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, point estimate, confidence interval, rural
                 spanish electrical energy distribution",
  ISSN =         "1089-778X",
  URL =          "",
  size =         "9 pages",
  abstract =     "When genetic programming (GP) methods are applied to
                 solve symbolic regression problems, we obtain a point
                 estimate of a variable, but it is not easy to calculate
                 an associated confidence interval. We designed an
                 interval arithmetic-based model that solves this
                 problem. Our model extends a hybrid technique, the GA-P
                 method, that combines genetic algorithms and genetic
                 programming. Models based on interval GA-P can devise
                 an interval model from examples and provide the
                 algebraic expression that best approximates the data.
                 The method is useful for generating a confidence
                 interval for the output of a model, and also for
                 obtaining a robust point estimate from data which we
                 know to contain outliers. The algorithm was applied to
                 a real problem related to electrical energy
                 distribution. Classical methods were applied first, and
                 then the interval GA-P. The results of both studies are
                 used to compare interval GA-P with GP, GA-P, classical
                 regression methods, neural networks, and fuzzy

Genetic Programming entries for Luciano Sanchez