Minimising Testing in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@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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