  Machine learning is a novel and powerful technology and has been widely used
in various science topics. We demonstrate a machine-learning based approach
built by a set of general metrics and rules inspired by physics. Taking
advantages of physical constraints, such as dimension identity, symmetry and
generalization, we succeed to rediscover the GellMann Okubo formula using a
technique of symbolic regression. This approach can effectively find explicit
solutions among user-defined observable, and easily extend to study on exotic
hadron spectrum.
