Reinforced Linear Genetic Programming
Neural and Evolutionary Computing
2026-01-16 v1 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2601.09736,
title = {Reinforced Linear Genetic Programming},
author = {Urmzd Mukhammadnaim},
journal= {arXiv preprint arXiv:2601.09736},
year = {2026}
}
Comments
Bachelor's thesis. Source code can be found at https://www.github.com/urmzd/linear-gp