GP with Ranging-Binding Technique for Symbolic Regression

This paper proposes a model-based genetic programming algorithm for symbolic regression, called the ranging-binding genetic programming algorithm (RBGP). The goal is to allow offspring to retain the superiority of their promising parents during evolution. Inspired by the concept of model building, RBGP makes use of syntactic information and semantics information in a program to capture the hidden patterns. When compared with GP-GOMEA, ellynGP, and gplearn, RBGP outperformed the others on average in the Penn machine learning benchmarks, RBGP achieving statistically significant improvements over all other methods on 44% of the problems.