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.

翻译：线性遗传编程（LGP）是一种强大的技术，它允许使用程序的线性表示来解决各种问题。然而，该技术仍存在一些局限性，例如需要人工显式地将寄存器映射到操作。本论文提出了一种新颖的方法，在LGP基础上使用Q学习，即强化线性遗传编程（RLGP），以学习最优的寄存器-操作分配。在此过程中，我们引入了一个用内存安全的Rust语言编写的新框架“linear-gp”，该框架为未来的研究工作提供了广泛的实验支持。