Article,
Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes
Journal of Computing in Civil Engineering, 18 (2): 145--153 (2004)
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.
