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Publication Detail
Minimising Testing in Genetic Programming
  • Publication Type:
    Report
  • Authors:
    Langdon WB
  • publication date:
    11/04/2011
  • Place of publication:
    Gower Street, London WC1E 6BT, UK
  • Report number:
    RN/11/10
  • Notes:
    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 notes: Technical report version of \citelangdon:2011:gecco size: 18 pages
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.
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