Surrogate modeling with Genetic Programming applied to satellite communication and ground stations

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Glen D. Rodriguez and Ivan Velasquez and 
                 Dane Cachi and Dante Inga",
  title =        "Surrogate modeling with Genetic Programming applied to
                 satellite communication and ground stations",
  booktitle =    "2012 IEEE Aerospace Conference",
  year =         "2012",
  address =      "Big Sky, MT, USA",
  month =        "3-10 " # mar,
  organisation = "IEEE, Aerospace and Electronic Systems Society - AES",
  publisher =    "Curran Associates",
  keywords =     "genetic algorithms, genetic programming, Cubesats,
                 DACE models, Doppler shift correction, MAE, RMSE,
                 computer aided design, evolutionary approach, ground
                 stations, hardware design, machine learning, maximum
                 absolute error, medium absolute error, neural networks,
                 nonstructured mathematical functions, orbital
                 calculation, root mean square error, satellite
                 communication, satellite missions, software design,
                 support vector machines, surrogate modelling, trees,
                 learning (artificial intelligence), mean square error
                 methods, satellite ground stations, telecommunication
                 computing, trees (mathematics)",
  isbn13 =       "9781457705564",
  ISSN =         "1095-323X",
  URL =          "",
  DOI =          "doi:10.1109/AERO.2012.6187326",
  size =         "8 pages",
  abstract =     "In satellite missions, there are many complex factors
                 requiring complex software or hardware design; for
                 example: orbital calculation, Doppler shift correction.
                 In optimisation and computer aided design, the use of
                 surrogate models has been increasing lately. These
                 models replace a complex calculation or simulation by a
                 simpler one, with good approximation. Neural Networks,
                 Support Vector Machines and DACE models have been used,
                 but Genetic Programming is another way to create
                 surrogate models and little research has been done
                 about it. An advantage of using simpler models in small
                 satellite missions, such as Cubesats, is that they are
                 less demanding regarding circuits (both in money and in
                 power consumption) and memory. If the approximation is
                 good, the surrogate model could be enough. These
                 savings could be multiplied by a factor of 20 or more
                 if the surrogate models are applied into constellations
                 of small satellites, with 20 or more individual
                 satellites involved. In this paper, Genetic programming
                 is compared against Neural Networks for creating
                 surrogate models for orbital calculations and Doppler
                 shift. The models are created by machine learning, that
                 is, the method takes a set of experimental or
                 calculated samples and it uses them to create a model
                 that approximates those samples. Genetic Programming
                 uses an evolutionary approach that evolves trees
                 representing non-structured mathematical functions
                 formed from a alphabet of basic operations (in this
                 paper: constants, +, -, *, /, sin, cos, log, exp). The
                 main metrics of success are the maximum absolute error,
                 the MAE (medium absolute error) and RMSE (root mean
                 square error) against a bigger set of validation
  notes =        "6 volumes
                 Also known as \cite{6187326}",

Genetic Programming entries for Glen D Rodriguez Rafael Ivan Christian Velasquez Aparco Dane Bruce Cachi Eugenio Dante Inga