Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem

Andrew Ni; Lee Spector

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arXiv · 2404.12750

Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem

Andrew Ni·Lee Spector

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Abstract

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.

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Andrew Ni, Lee Spector. 2024-04-19. Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem. https://arxiv.org/abs/2404.12750

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