# Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

arXiv: https://arxiv.org/abs/2606.31990 · HTML: https://mlsanity.com/paper/2606.31990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Contemporary Symbolic Regression Methods and their Relative Performance](https://mlsanity.com/paper/2107.14351/) (2021)
- [Symbolic Regression via Neural-Guided Genetic Programming Population Seeding](https://mlsanity.com/paper/2111.00053/) (2021)
- [A Bayesian machine scientist to aid in the solution of challenging scientific problems](https://mlsanity.com/paper/2004.12157/) (2020)
- [Exhaustive Symbolic Regression](https://mlsanity.com/paper/2211.11461/) (2022)
- [Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression](https://mlsanity.com/paper/2107.10640/) (2021)
- [Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions](https://mlsanity.com/paper/2605.22374/) (2026)

## Abstract (arXiv)

We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as well as initialization with small optimized solutions from exhaustive symbolic regression (ESR) using a GP/SR implementation which is based on the multi-objective evolutionary algorithm NSGA-II. We compare the final Pareto fronts found with each initialization method on twelve synthetic problems of varying complexity and one real-world dataset. We find no significant differences in accuracy or model complexity among the initialization methods. The initial advantage of initialization with ESR disappears after only a few generations. Our results show that, given similar diversity in the initial population, the effect of the initialization method in GP-based symbolic regression on the final Pareto front is negligible.