# Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

## TL;DR

This paper introduces the Interaction-Transformation (IT) representation for symbolic regression, which simplifies the search space and improves approximation accuracy over traditional genetic programming methods.

## Contribution

It presents a new IT-based evolutionary algorithm that evolves expressions solely through mutation, demonstrating superior performance on real-world data sets.

## Key findings

- IT representation creates a smoother search space
- The algorithm outperforms traditional genetic programming
- IT approach finds better data approximations

## Abstract

The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.