TY  - EJOU
AU  - Pan, Jeng-Shyang 
AU  - Cao, Cuijing 
AU  - Chu, Shu-an 
AU  - Kong, Lingping 
AU  - Xue, Xingsi 
AU  - Zhao, Jia 

TI  - Genetic Programming-Based Search Strategy Generation Applied to Emergency Material Transportation Scheduling
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - 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.
KW  - Genetic programming; tumbleweed algorithm; search strategy generation; emergency scheduling

DO  - 10.32604/cmc.2026.085803