Genetic programming applied to RFI mitigation in radio astronomy

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@MastersThesis{Staats:2016:mastersthesis,

• author = "Kai Staats",
• keywords = "genetic algorithms, genetic programming",
• title = "Genetic programming applied to RFI mitigation in radio astronomy",
• school = "University of Cape town",
• year = "2016",
• type = "Master of Science",
• address = "South Africa",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://open.uct.ac.za/handle/11427/23703",
• URL = "https://open.uct.ac.za/bitstream/item/26627/thesis_sci_2016_staats_kai.pdf",
• size = "153 pages",
• abstract = "Genetic Programming is a type of machine learning that employs a stochastic search of a solutions space, genetic operators, a fitness function, and multiple generations of evolved programs to resolve a user-defined task, such as the classification of data. At the time of this research, the application of machine learning to radio astronomy was relatively new, with a limited number of publications on the subject. Genetic Programming had never been applied, and as such, was a novel approach to this challenging arena. Foundational to this body of research, the application Karoo GP was developed in the programming language Python following the fundamentals of tree-based Genetic Programming described in A Field Guide to Genetic Programming by Poli, et al. \cite{poli08:fieldguide}. Karoo GP was tasked with the classification of data points as signal or radio frequency interference (RFI) generated by instruments and machinery which makes challenging astronomers ability to discern the desired targets. The training data was derived from the output of an observation run of the KAT-7 radio telescope array built by the South African Square Kilometre Array (SKA-SA). Karoo GP, kNN, and SVM were comparatively employed, the outcome of which provided noteworthy correlations between input parameters, the complexity of the evolved hypotheses, and performance of raw data versus engineered features. This dissertation includes description of novel approaches to GP, such as upper and lower limits to the size of syntax trees, an auto-scaling multiclass classifier, and a Numpy array element manager. In addition to the research conducted at the SKA-SA, it is described how Karoo GP was applied to fine-tuning parameters of a weather prediction model at the South African Astronomical Observatory (SAAO), to glitch classification at the Laser Interferometer Gravitational-wave Observatory (LIGO), and to astro-particle physics at The Ohio State University.",
• notes = "Supervised by Prof. Bruce Bassett,",

}

Genetic Programming entries for Kai Staats

Citations