How Noisy Data Affects Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Miranda:2017:GECCOa,

• 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 Programming",
• 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 = "http://doi.acm.org/10.1145/3071178.3071300",
• 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 regression",
• 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

Citations