Using weighted genetic programming to program squat wall strengths and tune associated formulas

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

@Article{Tsai2011526,
  author =       "Hsing-Chih Tsai",
  title =        "Using weighted genetic programming to program squat
                 wall strengths and tune associated formulas",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "24",
  number =       "3",
  pages =        "526--533",
  year =         "2011",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2010.08.010",
  URL =          "http://www.sciencedirect.com/science/article/B6V2M-512KGGT-1/2/19ea4426ab2d8ed33e75c91b78297d2f",
  keywords =     "genetic algorithms, genetic programming, Weighted
                 formulae, Prediction, Squat wall strength",
  abstract =     "This study developed a weighted genetic programming
                 (WGP) approach to study the squat wall strength. The
                 proposed WGP evolves on genetic programming (GP), an
                 evolutionary algorithm-based methodology that employs a
                 binary tree topology and optimised functional
                 operators. Weight coefficients were introduced to each
                 GP linkage in the tree in order to create a new
                 weighted genetic programming (WGP) approach. The
                 proposed WGP offers two distinct advantages, including:
                 (1) a balance of influences is struck between the two
                 front input branches and (2) weights are incorporated
                 throughout generated formulae. Resulting formulae
                 contain a certain quantity of optimised functions and
                 weights. Genetic algorithms are employed to accomplish
                 WGP optimisation of function selection and proper
                 weighting tasks. Case studies herein focused on a
                 reference study of squat wall strength. Results
                 demonstrated that the proposed WGP provides accurate
                 results and formula outputs. This paper further used
                 WGP to tune referenced formulas, which yielded a final
                 formula that combined the positive attributes of both
                 WGP and analytical models.",
}

Genetic Programming entries for Hsing-Chih Tsai

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