Genetic Algorithms for Real Parameter Optimization
Given data in the form of a collection of (x,y) pairs of
real numbers, the symbolic function identification problem is to
find a functional model of the form y = f(x) that fits the data.
This paper describes a system for solution of symbolic function
identification problems that combines a genetic algorithm and the
Levenberg-Marquardt nonlinear regression algorithm. The genetic
algorithm uses an expression-tree representation rather than the
more usual binary-string representation. Experiments were run
with data generated using a wide variety of function models. The
system was able to find a function model that closely
approximated the data with a very high success rate.