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

Created by W.Langdon from gp-bibliography.bib Revision:1.8638

@InProceedings{DBLP:conf/cec/ZhouC00RGK25,

• author = "Yun Zhou and Gang Chen and Bing Xue and Mengjie Zhang and Jeremy S. Rooney and Keith C. Gordon and Daniel P. Killeen",
• title = "Genetic Programming for High-Level Multi-Spectral Data Fusion in Fish Biochemical Analysis",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Industries, Spectroscopy, Technological innovation, Accuracy, Stacking, Data integration, Predictive models, Fish, Optimization, machine learning, fish biochemical composition analysis, data fusion",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Tue, 01 Jul 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/ZhouC00RGK25.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11043119",
• DOI = "10.1109/CEC65147.2025.11043119",
• abstract = "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 optimise 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 6.9percent, 8.0percent, and 2.6percent, 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.",
• notes = "also known as \cite{zhou:2025:CEC} \cite{11043119}",

}

Genetic Programming entries for Yun Zhou Gang Chen Bing Xue Mengjie Zhang Jeremy S Rooney Keith C Gordon Daniel P Killeen

Citations