  Symbolic regression aims to find a function that best explains the
relationship between independent variables and the objective value based on a
given set of sample data. Genetic programming (GP) is usually considered as an
appropriate method for the problem since it can optimize functional structure
and coefficients simultaneously. However, the convergence speed of GP might be
too slow for large scale problems that involve a large number of variables.
Fortunately, in many applications, the target function is separable or
partially separable. This feature motivated us to develop a new method, divide
and conquer (D&C), for symbolic regression, in which the target function is
divided into a number of sub-functions and the sub-functions are then
determined by any of a GP algorithm. The separability is probed by a new
proposed technique, Bi-Correlation test (BiCT). D&C powered GP has been tested
on some real-world applications, and the study shows that D&C can help GP to
get the target function much more rapidly.
