Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes

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@Article{yang:145,
  author =       "C. X. Yang and L. G. Tham and X. T. Feng and 
                 Y. J. Wang and P. K. K. Lee",
  title =        "Two-Stepped Evolutionary Algorithm and Its Application
                 to Stability Analysis of Slopes",
  publisher =    "ASCE",
  year =         "2004",
  journal =      "Journal of Computing in Civil Engineering",
  volume =       "18",
  number =       "2",
  pages =        "145--153",
  keywords =     "genetic algorithms, genetic programming, civil
                 engineering computing, stability criteria, failure
                 analysis",
  URL =          "http://link.aip.org/link/?QCP/18/145/1",
  DOI =          "doi:10.1061/(ASCE)0887-3801(2004)18:2(145)",
  abstract =     "Based on genetic algorithm and genetic programming, a
                 new evolutionary algorithm is developed to evolve
                 mathematical models for predicting the behavior of
                 complex systems. The input variables of the models are
                 the property parameters of the systems, which include
                 the geometry, the deformation, the strength parameters,
                 etc. On the other hand, the output variables are the
                 system responses, such as displacement, stress, factor
                 of safety, etc. To improve the efficiency of the
                 evolution process, a two-stepped approach is adopted;
                 the two steps are the structure evolution and parameter
                 optimization steps. In the structure evolution step, a
                 family of model structures is generated by genetic
                 programming. Each model structure is a polynomial
                 function of the input variables. An interpreter is then
                 used to construct the mathematical expression for the
                 model through simplification, regularization, and
                 rationalization. Furthermore, necessary internal model
                 parameters are added to the model structures
                 automatically. For each model structure, a genetic
                 algorithm is then used to search for the best values of
                 the internal model parameters in the parameter
                 optimization step. The two steps are repeated until the
                 best model is evolved. The slope stability problem is
                 used to demonstrate that the present method can
                 efficiently generate mathematical models for predicting
                 the behavior of complex engineering systems.",
  notes =        "also known as \cite{CXYang:2004:JCCE} c2004 American
                 Society of Civil Engineers",
}

Genetic Programming entries for Chengxiang Yang L G Tham Xia-Ting Feng Y J Wang P K K Lee

Citations