Genetic programming applied to RFI mitigation in radio astronomy

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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