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.