Reinforced Linear Genetic Programming
cs.NE, cs.AI
Submitted: 2026-01-07
Updated: 2026-10-20
Comments: Bachelor's thesis. Source code can be found at https://www.github.com/urmzd/linear-gp. v2: fixes HTML rendering
License: http://creativecommons.org/licenses/by/4.0/
The gist: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs.
Terminology
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.
Related papers
- Evolutionary Ensemble of Agents
- Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
- Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
- Evolutionary Architecture Search for Chlorophyll- a Prediction in Lakes using Sentinel-2
- Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
- Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs