Minimising Testing in Genetic Programming

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

@TechReport{langdon:2011:geccoRN,
  author =       "W. B. Langdon",
  title =        "Minimising Testing in Genetic Programming",
  institution =  "Computer Science, University College London",
  year =         "2011",
  number =       "RN/11/10",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        "11 " # apr,
  keywords =     "genetic algorithms, genetic programming, search,
                 heuristic methods, artificial intelligence, software
                 engineering, theory, over fitting, evolutionary
                 learning, deceptive fitness landscapes, population
                 convergence, correlations, GPU, GPGPU, 11-Mux, 20-mux,
                 37-multiplexor, bloat",
  URL =          "http://www-typo3.cs.ucl.ac.uk/fileadmin/UCL-CS/images/Research_Student_Information/RN_11_10.pdf",
  abstract =     "The cost of optimisation can be reduced by evaluating
                 candidate designs on only a fraction of all possible
                 use cases. We show how genetic programming (GP) can
                 avoid overfitting and evolve general solutions from
                 fitness test suites as small as just one dynamic
                 training case. Search effort can be greatly reduced.",
  notes =        "Technical report version of
                 \cite{langdon:2011:gecco}",
  size =         "18 pages",
}

Genetic Programming entries for William B Langdon

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