<p>This paper introduces a novel approach called semantic boosting regression (SBR), leveraging the principles of boosting algorithms in symbolic regression using a Memetic Semantic GP for Symbolic Regression (MSGP) algorithm as weak learners. Memetic computation facilitates the integration of domain knowledge into a population-based approach, and semantic-based algorithms enhance local improvements to achieve targeted outputs. The fusion of memetic and semantic approaches allows us to augment the exploration and exploitation capabilities inherent in Genetic Programming (GP) and identify concise symbolic expressions that maintain interpretability without compromising the expressive power of symbolic regression. Our approach echoes the boosting algorithm’s characteristic, where weak learners (e.g., MSGP) are sequentially improved upon, focusing on correcting previous errors and continuously enhancing overall performance. This iterative strategy, intrinsic to boosting methods, is adeptly adapted to our SBR model. Experimental results demonstrate that our memetic-semantic approach has equal or better performance when compared to state-of-the-art evolutionary-based techniques when addressing real-world symbolic regression challenges. This advancement helps tackle the bloating issue in GP and significantly improves generalization capabilities. However, akin to classic boosting algorithms, one limitation of our approach is the increased computational cost due to the sequential training of boosting learners.</p>