Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem
<|reference_start|>Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem: The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems. In this paper, we use symbolic regression to learn useful features of near-optimal packing plans, which we then use to design efficient metaheuristic genetic algorithms for the traveling thief algorithm. By using symbolic regression again to initialize the metaheuristic GA with near-optimal individuals, we are able to design a fast, interpretable, and effective packing initialization scheme. Comparisons against previous initialization schemes validates our algorithm design.<|reference_end|>
@article{ni2024leveraging,
    title={Leveraging Symbolic Regression for Heuristic Design in the Traveling
  Thief Problem},
    author={Andrew Ni, Lee Spector},
    journal={arXiv preprint arXiv:2404.12750},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12750},
    primaryClass={cs.NE}
}