Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem

TL;DR

This paper introduces a symbolic regression-based approach to improve heuristic algorithms for the NP-hard Traveling Thief Problem by learning features of packing plans and initializing genetic algorithms, resulting in faster and more effective solutions.

Contribution

It presents a novel method using symbolic regression to create interpretable, effective initialization schemes for metaheuristic algorithms tackling the Traveling Thief Problem.

Findings

Improved packing plan initialization leads to better solutions.

Symbolic regression effectively learns features of near-optimal packing.

The proposed method outperforms previous initialization schemes.

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.