# Reinforced Linear Genetic Programming

arXiv: https://arxiv.org/abs/2601.09736 · HTML: https://mlsanity.com/paper/2601.09736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework](https://mlsanity.com/paper/2201.13305/) (2022)

## Abstract (arXiv)

Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to actions. This thesis proposes a novel approach that uses Q-Learning on top of LGP, Reinforced Linear Genetic Programming (RLGP) to learn the optimal register-action assignments. In doing so, we introduce a new framework "linear-gp" written in memory-safe Rust that allows for extensive experimentation for future works.