Title: Augmenting Genetic Programming search with Deep Learning Models for Symbolic Regression
Abstract: Symbolic Regression (SR) has recently gained momentum within the scientific community as it can yield accurate, yet more interpretable results w.r.t. black-box models. The two core state-of-the-art methods for solving SR, namely Genetic Programming (GP) and deep-learning models (DLM), both suffer from issues that hinder their large-scale applicability.
GP models are scalable and precise, but due to their lack of locality they require a lot of computational power (and time) to achieve good results, whereas DLMs provide solutions significantly faster, yet are not able to scale to large real-world problems.
In this talk, I will provide some insights into the ongoing work targeted at combining GP and DLMs into an efficient and scalable search algorithm for solving SR.
