Revisiting Interval Arithmetic for Regression Problems in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4014

@InProceedings{Dick:2017:GECCOa,
  author =       "Grant Dick",
  title =        "Revisiting Interval Arithmetic for Regression Problems
                 in Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "129--130",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3076107",
  DOI =          "doi:10.1145/3067695.3076107",
  acmid =        "3076107",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, interval
                 arithmetic, symbolic regression",
  month =        "15-19 " # jul,
  abstract =     "Traditional approaches to symbolic regression require
                 the use of protected operators, which can lead to
                 perverse model characteristics and poor generalisation.
                 In this paper, we revisit interval arithmetic as one
                 possible solution to allow genetic programming to
                 perform regression using unprotected operators. Using
                 standard benchmarks, we show that using interval
                 arithmetic within model evaluation does not prevent
                 invalid solutions from entering the population, meaning
                 that search performance remains compromised. We extend
                 the basic interval arithmetic concept with safe search
                 operators that integrate interval information into
                 their process, thereby greatly reducing the number of
                 invalid solutions produced during search. The resulting
                 algorithms are able to more effectively identify good
                 models that generalise well to unseen data.",
  notes =        "Extended version on
                 https://arxiv.org/abs/1704.04998

                 Also known as \cite{Dick:2017:RIA:3067695.3076107}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Grant Dick

Citations