Improving genetic programming based symbolic regression using deterministic machine learning

Symbolic regression (SR) is a well studied method in genetic programming (GP) for discovering free-form mathematical models from observed data. However, it has not been widely accepted as a standard data science tool. The reluctance is in part due to the hard to analyze random nature of GP and scalability issues. On the other hand, most popular deterministic regression algorithms were designed to generate linear models and therefore lack the flexibility of GP based SR (GP-SR). Our hypothesis is that hybridizing these two techniques will create a synergy between the GP-SR and deterministic approaches to machine learning, which might help bring the GP based techniques closer to the realm of big learning. In this paper, we show that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.