A Hierarchical Genetic System for Symbolic Function Identification, (with Minga Jiang), Proceedings of the Interface 92 Conference on the Interface between Computer Science and Statistics. (1992).

Abstract
     
     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.