A divide and conquer method for symbolic regression

Understand

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.