Reinforced Linear Genetic Programming

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.

Bachelor's thesis. Source code can be found at this https URL. v2: fixes HTML rendering

Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)

arXiv:2601.09736 [cs.NE]

(or arXiv:2601.09736v2 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2601.09736 Focus to learn more arXiv-issued DOI via DataCite

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