Multi-target Genetic Programming for Nutrient Assessment in Fish

The Cyber-Marine project aims to maximise the utility of commercial fish by using Artificial Intelligence (AI) supported tools and non-invasive chemical methods to assess their nutrient content. Vibrational Spectroscopy has proven to be a reliable method to determine molecular characteristics of a product. Particularly, it has gained prominence in evaluating the nutrient level of produce through statistical modelling techniques, such as Partial Least Squared regression (PLSR). This thesis investigates the use of Machine Learning (ML) to relate spectral data of fish with their nutrient levels. Within ML algorithms, Genetic Programming for symbolic regression (GPSR) emerges as a notable approach, producing models that exhibit generalisability and interpretability. As an extension of GPSR, multi-tree GP (MTGP), provides a platform that can simultaneously train models for several nutrients or bioactive components. In this thesis, significant enhancements and modifications are made to key aspects of MTGP, including its evolutionary process, by the introduction of new genetic operator, and the evaluation function, through the use of a distance metric. These are designed not only to leverage the relationship between the spectral information and bioactive components, but also to optimise the interplay between different target variables thereby maximising the capabilities of the algorithm.