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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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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