Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic

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

  author =       "Cassio Pennachin and Moshe Looks and 
                 J. A. {de Vasconcelos}",
  title =        "Improved Time Series Prediction and Symbolic
                 Regression with Affine Arithmetic",
  booktitle =    "Genetic Programming Theory and Practice IX",
  year =         "2011",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Jason H. Moore",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  publisher =    "Springer",
  chapter =      "6",
  pages =        "97--112",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, time series prediction, wind forecasting,
                 affine arithmetic, robustness",
  isbn13 =       "978-1-4614-1769-9",
  DOI =          "doi:10.1007/978-1-4614-1770-5_6",
  abstract =     "We show how affine arithmetic can be used to improve
                 both the performance and the robustness of genetic
                 programming for problems such as symbolic regression
                 and time series prediction. Affine arithmetic is used
                 to estimate conservative bounds on the output range of
                 expressions during evolution, which allows us to
                 discard trees with potentially infinite bounds, as well
                 as those whose output range lies outside the desired
                 range implied by the training dataset. Benchmark
                 experiments are performed on 15 symbolic regression
                 problems as well as 2 well known time series problems.
                 Comparison with a baseline genetic programming system
                 shows a reduced number of fitness evaluations during
                 training and improved generalisation on test data,
                 completely eliminating extreme errors. We also apply
                 this technique to the problem of forecasting wind speed
                 on a real world dataset, and the use of affine
                 arithmetic compares favourably with baseline genetic
                 programming, feed forward neural networks and support
                 vector machines.",
  notes =        "part of \cite{Riolo:2011:GPTP}",
  affiliation =  "Universidade Federal de Minas Gerais, Belo Horizonte,
                 MG, Brazil",

Genetic Programming entries for Cassio Pennachin Moshe Looks Joao Antonio de Vasconcelos