Open Access

ARTICLE

Genetic Programming-Based Search Strategy Generation Applied to Emergency Material Transportation Scheduling

Jeng-Shyang Pan^1,2,3, Cuijing Cao⁴, Shu-Chuan Chu^2,*, Lingping Kong⁵, Xingsi Xue⁶, Jia Zhao⁷

1 School of Information Engineering, Yango University, Fuzhou, China
2 School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, China
3 Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
4 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
5 Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech Republic
6 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
7 School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang, China

* Corresponding Author: Shu-Chuan Chu. Email:

Computers, Materials & Continua 2026, 89(2), 45 https://doi.org/10.32604/cmc.2026.085803

Received 18 May 2026; Accepted 28 July 2026; Issue published 15 September 2026

Abstract

This study proposes a Genetic Programming-based Search Strategy Generation Framework (GP-SSGF) and a novel variant of the tumbleweed algorithm, the genetic programming-based tumbleweed algorithm (GPTA). The framework automates the evolution of search formulas within metaheuristic algorithms, reducing reliance on manually designed update rules and enhancing adaptability. The GPTA algorithm, developed within this framework, employs evolved position-update formulas to improve search efficiency and convergence. Through extensive experiments on the CEC2017 benchmark suite across multiple dimensions, GPTA demonstrates superior solution quality and stability compared with other metaheuristic algorithms. Its practical effectiveness is further validated in emergency material transportation scheduling, where it optimizes resource dispatching under multiple constraints, significantly reducing costs. This research highlights GPTA’s potential to solve complex optimization problems and its broad applicability across fields such as emergency management.

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Keywords

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