Block building programming for symbolic regression

TL;DR

This paper introduces block building programming (BBP), a novel method for symbolic regression that improves efficiency by dividing models into blocks and factors, reducing search space and accelerating convergence.

Contribution

The paper proposes BBP, a new approach that leverages variable separability to enhance symbolic regression efficiency and accuracy, outperforming traditional methods like genetic programming.

Findings

BBP significantly reduces search space and improves convergence speed.

BBP achieves high accuracy in structure and coefficient optimization.

Numerical results demonstrate BBP's superior computational efficiency.

Abstract

Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modeled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency.