# TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming

arXiv: https://arxiv.org/abs/2504.10253 · HTML: https://mlsanity.com/paper/2504.10253/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [Contemporary Symbolic Regression Methods and their Relative Performance](https://mlsanity.com/paper/2107.14351/) (2021)
- [Where are we now? A large benchmark study of recent symbolic regression methods](https://mlsanity.com/paper/1804.09331/) (2018)
- [PSB2: The Second Program Synthesis Benchmark Suite](https://mlsanity.com/paper/2106.06086/) (2021)

## Built on by (most cited first, 2 of 2)

- [Improving Evaluation of Recombination-based Cartesian Genetic Programming](https://mlsanity.com/paper/2605.28353/) (2026)
- [Runtime Analysis of Cartesian Genetic Programming in Evolving Boolean Functions](https://mlsanity.com/paper/2606.15923/) (2026)

## Abstract (arXiv)

Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, it is capable of tackling multiple problem domains. Current benchmarking initiatives are fragmented, as the different representations are not compared with each other and their performance is not measured across the different domains. In this work, we propose a unified framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to multiple representations and problem domains, including symbolic regression, logic synthesis and policy search.