{
  "annotation_id": "8311cbcc-eee8-429c-b71c-370c16994c5b",
  "date_created": "2026-02-17T05:53:22.990000Z",
  "date_modified": "2026-02-17T05:53:22.990000Z",
  "file_hash": "abee883dfcf8caa48f68ee05643b5486a28b9c7c30b1f61084703c4804605f3f",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.09736",
    "authors": [
      "Urmzd Mukhammadnaim"
    ],
    "categories": [
      "cs.NE",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Reinforced Linear Genetic Programming",
    "url": "https://arxiv.org/abs/2601.09736",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ba68cb7c-6c4e-4ab3-ad5d-bd7db6a65dbf",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}