How Noisy Data Affects Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4031

  author =       "Luis F. Miranda and Luiz Otavio V. B. Oliveira and 
                 Joao Francisco B. S. Martins and Gisele L. Pappa",
  title =        "How Noisy Data Affects Geometric Semantic Genetic
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "985--992",
  size =         "8 pages",
  URL =          "",
  DOI =          "doi:10.1145/3071178.3071300",
  acmid =        "3071300",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, geometric
                 semantic genetic programming, noise impact, symbolic
  month =        "15-19 " # jul,
  abstract =     "Noise is a consequence of acquiring and pre-processing
                 data from the environment, and shows fluctuations from
                 different sources, e.g., from sensors, signal
                 processing technology or even human error. As a machine
                 learning technique, Genetic Programming (GP) is not
                 immune to this problem, which the field has frequently
                 addressed. Recently, Geometric Semantic Genetic
                 Programming (GSGP), a semantic-aware branch of GP, has
                 shown robustness and high generalization capability.
                 Researchers believe these characteristics may be
                 associated with a lower sensibility to noisy data.
                 However, there is no systematic study on this matter.
                 This paper performs a deep analysis of the GSGP
                 performance over the presence of noise. Using 15
                 synthetic datasets where noise can be controlled, we
                 added different ratios of noise to the data and
                 compared the results obtained with those of a canonical
                 GP. The results show that, as we increase the
                 percentage of noisy instances, the generalization
                 performance degradation is more pronounced in GSGP than
                 GP. However, in general, GSGP is more robust to noise
                 than GP in the presence of up to 10percent of noise,
                 and presents no statistical difference for values
                 higher than that in the test bed.",
  notes =        "Also known as \cite{Miranda:2017:NDA:3071178.3071300}
                 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 Luis Fernando Miranda Luiz Otavio Vilas Boas Oliveira Joao Francisco B S Martins Gisele L Pappa