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.
@article{arxiv.2404.12750,
title = {Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem},
author = {Andrew Ni and Lee Spector},
journal= {arXiv preprint arXiv:2404.12750},
year = {2024}
}