Genetic Programming for High-Level Multi-Spectral Data Fusion in Fish Biochemical Analysis

Accurate assessment of biochemical compositions in fish products is essential for quality control in the seafood industry and nutritional research. While spectroscopic techniques enable non-destructive analysis, each method has limitations in prediction accuracy and reliability. Multi-modal data fusion offers a promising solution, but developing robust fusion strategies remains challenging due to complex relationships between spectral features and biochemical properties. This paper presents GP-Fusion, a genetic programming-based high-level fusion method that integrates multiple spectroscopic modalities. Unlike conventional approaches, GPFusion evolves interpretable fusion functions to optimize predictions from diverse spectroscopy work-flows. A key innovation is the replicate variance penalty, which enhances prediction consistency across replicate measurements by capturing within-sample variability and mitigating batch effects. Experimental evaluations on three biochemical targets, including Omega-3, Omega-6, and monounsaturated fatty acids, show that GPFusion improves the coefficient of determination by 6.9%, 8.0%, and 2.6%, respectively. Compared with other high-level fusion strategies, GPFusion delivers more stable predictions with lower variance while maintaining competitive accuracy. Additional empirical studies confirms the effectiveness of the replicate variance penalty and reveal critical trade-offs between tree depth and terminal flexibility for evolving compact and interpretable fusion functions.