arXiv cs.AI· Urmzd Mukhammadnaim·· 12 小时前AI 评分18
强化线性遗传编程（RLGP）：用 Q-Learning 学习寄存器-动作映射
Reinforced Linear Genetic Programming
AI 导读
一篇学士论文提出 Reinforced Linear Genetic Programming（RLGP），在 Linear Genetic Programming 之上叠加 Q-Learning，自动学习最优的寄存器-动作分配，替代人工显式映射。作者同时发布用内存安全的 Rust 编写的 "linear-gp" 框架，供后续实验使用，代码已公开。
正文 · 原文
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.
| Comments: | Bachelor's thesis. Source code can be found at this https URL. v2: fixes HTML rendering |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2601.09736 [cs.NE] |
| (or arXiv:2601.09736v2 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2601.09736 arXiv-issued DOI via DataCite |
Submission history
From: Urmzd Mukhammadnaim [view email]
[v1]
Wed, 7 Jan 2026 04:15:16 UTC (1,005 KB)
[v2]
Tue, 20 Oct 2026 02:50:38 UTC (1,000 KB)
来源：arXiv cs.AI · arxiv.org