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Congrats Jeremy

Congratulations to Jeremy and Keith on their latest publication in the Journal of the Royal Society of New Zealand.  The citaiton is: Huang, Z.; Xue, B.; Zhang, M.; Rooney, J. S.; Gordon, K. C.; Killeen, D. P. Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method With Tunable Primitives. Journal of the Royal Society of New Zealand 2026, 56 (3), e70051. DOI: https://doi.org/10.1002/snz2.70051

The work describes the use of machine learning based around genetic algorithms to classify fish waste and was part of an MBIE program looking at developing new technologies in marine industries.

 

Applying linear genetic programming with tunable primitives (LGP-TP) for fish biomass component prediction. Particularly, LGP-TP
synthesizes regression models based on the spectral data (inputs) and the chemical ground truth of on-hand fish samples (target outputs). The synthesized regression model from LGP-TP predicts the components of unseen fish biomass samples in real-world production.