How Noisy Data Affects Geometric Semantic Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Miranda:2017:GECCOa,
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author = "Luis F. Miranda and Luiz Otavio V. B. Oliveira and
Joao Francisco B. S. Martins and Gisele L. Pappa",
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title = "How Noisy Data Affects Geometric Semantic Genetic
Programming",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "985--992",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071300",
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DOI = "doi:10.1145/3071178.3071300",
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acmid = "3071300",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, geometric
semantic genetic programming, noise impact, symbolic
regression",
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month = "15-19 " # jul,
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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.",
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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
Citations