# A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming

## TL;DR

This paper introduces Neuro-encoded Expression Programming (NEEP), a neural network-based symbolic regression method that creates a smooth, continuous search space for genetic programming, improving efficiency and accuracy.

## Contribution

It proposes a novel neuro-encoding mechanism using RNNs to enhance genetic programming for symbolic regression, addressing issues of discrete solution space and landscape ruggedness.

## Key findings

- Improved training efficiency on symbolic regression tasks.
- Reduced test errors compared to traditional methods.
- Smoother fitness landscapes facilitate better search performance.

## Abstract

Neuro-encoded expression programming(NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expression programming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.