@Article{cmes.2026.084683,
AUTHOR = {Jeng-Shyang Pan, Wenda Li, Shu-Chuan Chu, Zhi-Gang Du, Hongmei Yang, Lingping Kong},
TITLE = {Adaptive Evolution of Metaheuristic Update Strategies Using Genetic Programming for Remote Sensing Image Fusion<sup></sup>},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v148n2/68574},
ISSN = {1526-1506},
ABSTRACT = {Formulating efficient updating techniques is essential for the efficacy of metaheuristic algorithms. Traditional approaches, however, depend significantly on manually developed formulas and empirical intuition, which frequently constrain their adaptability and scalability across various optimization tasks. This research introduces a Genetic Programming-based Metaheuristic framework, referred to as GP-MAs, designed to autonomously develop and enhance symbolic update rules for metaheuristic algorithms. Within the suggested GP-MAs architecture, genetic programming (GP) is integrated into the learning phase of the Growth Optimizer (GO) to dynamically formulate symbolic update equations, hence enhancing the algorithm’s adaptability to diverse optimization landscapes. A hybrid algorithm, termed GPbasedGO, is constructed based on GO to validate the effectiveness of the proposed framework. Comprehensive experiments on the CEC2022 benchmark suite illustrate the competitive convergence characteristics, optimization efficacy, and competitive performance across benchmark problems of GPbasedGO in comparison to many prominent state-of-the-art techniques. The practical usability of the proposed approach is assessed in a multispectral and panchromatic image fusion challenge, where GPbasedGO attains competitive results across many image quality measures, including ERGAS, SAM, RMSE, UIQI, and CC. The findings demonstrate that the GP-MAs framework offers a versatile and semi-automated model for metaheuristic optimization, proficient in producing adaptive symbolic updating techniques for key optimization problems. This study presents a comprehensive validation of GP-assisted symbolic strategy evolution for metaheuristic optimization and suggests interesting avenues for further research in adaptive optimization.},
DOI = {10.32604/cmes.2026.084683}
}