Identification of response surface models using
genetic programming
Mechanical Systems and Signal Processing, 20 (8):
1819--1831 (November 2006)DOI: doi:10.1016/j.ymssp.2005.12.003
Abstract
There is a move in modern research in Structural
Dynamics towards analysing the inherent uncertainty in
a given problem. This may be quantifying or fusing
uncertainty models, or can be propagation of
uncertainty through a system or calculation. If the
system of interest is represented by, e.g. a large
Finite Element (FE) model the large number of
computations involved can rule out many approaches due
to the expense of carrying out many runs. One way of
circumnavigating this problem is to replace the true
system by an approximate surrogate/replacement model,
which is fast-running compared to the original. In
traditional approaches using response surfaces a simple
least-squares multinomial model is often adopted. The
objective of this paper is to extend the class of
possible models considerably by carrying out a general
symbolic regression using a Genetic Programming
approach. The approach is demonstrated on both
univariate and multivariate problems with both
computational and experimental data.