Genetic Programming with Ranging-Binding Mechanism for Symbolic Regression
Journal
Communications in Computer and Information Science
Journal Volume
2828 CCIS
Start Page
37
End Page
51
ISSN
1865-0929
1865-0937
ISBN (of the container)
978-303215634-1
ISBN
9783032156341
9783032156358
Date Issued
2026-02-01
Author(s)
Abstract
This paper presents ranging-binding genetic programming (RBGP), a novel algorithm for symbolic regression. RBGP uses both syntax and semantics to detect hidden structural patterns and preserves advantageous traits from parent programs during evolution. The algorithm consists of two main components: binding and ranging. The binding mechanism detects frequently occurring two-layer substructures and prevents their disruption during recombination, thus retaining essential building blocks. The ranging mechanism modifies the output range of subtrees through tree replacement to align with the target value range. We evaluate RBGP against several state-of-the-art and widely used genetic programming methods, including GP-GOMEA, ellynGP, ellynGP with epsilon-lexicase selection, ellynGP with age-fitness Pareto optimization, and gplearn. Experiment results show that RBGP achieves the lowest average mean absolute error on 30 out of 37 benchmarks from the Penn machine learning benchmark, which demonstrates its generalization across diverse problem domains. An ablation study confirms the individual contributions of the binding and ranging mechanisms.
Event(s)
17th International Joint Conference on Computational Intelligence, IJCCI 2025
Subjects
Genetic Programming
Symbolic Regression
Publisher
Springer Nature Switzerland
Type
conference paper