Post by Fabricio Olivetti de França (Scholar, Linkedin)

In the last post we introduced the idea of e-graphs and how it can play an important role with equation discovery (aka symbolic regression). We also introduced eggp [1], the first equation discovery algorithm that takes advantage of e-graphs by using it as a powerful database system and enforce novelty.

We also briefly introduced r🥚ression [2], a Python tool that allows us to explore the power of e-graphs in different scenario. In this post, we will play a bit more with this tool to show how powerful e-graphs can be as a go to tool for equation discovery.

For a gentle introduction to e-graphs and equality saturation, see the previous part of this blog post.

This guest post by Fabrício Olivetti de França describes the concepts of Equality Graphs and Equality Saturation and the benefits of using E-Graphs in Symbolic Regression.

This year's workshop on Symbolic Regression at GECCO (Genetic and Evolutionary Computation Conference) saw a record number of submissions and was very well recieved. We had two marvelous sessions with nine contributed talks and a 30-minute long lively discussion round. The overall quality of talks was high spanning a good mix of topics including benchmarking, efficiency improvements, theoretical considerations, and applications. Thanks to all the speakers and participants for their contributions.

The meeting on Symbolic Regression in the Physical Sciences was held on 28th and 29th of April 2025 at Royal Society in London. Two days of insightful talks highlighted several applications of symbolic regression and gave some hints about future developments of symbolic regression methods. We provide our personal summary of the main topics in this post.