The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Matching methods are of great importance since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research on the development of new methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model specifically trained to find the mathematical expression that can solve the ground truth accurately, with the possibility of being understood by a human operator and forcing the processor to consume as little energy as possible. The experimental evaluation results show that our approach seems to be promising.