A Hybrid GP Approach for Numerically Robust Symbolic Regression
Günther Robert Raidl · 2002
This article introduces a hybrid variant of genetic programming (GP) for doing symbolic regression. Instead of the usual interpretation of a parse tree, all top-level terms are identified and extended by multiplying them with locally optimized factors. These weighted terms are then linearly combined to form the resulting expression. When using the mean square error as fitness function, local optimization of the factors can be done efficiently by applying a robust variant of the method of least squares.