Health prediction for king salmon via evolutionary machine learning with genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Fangfang_Zhang:JRSNZ,
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author = "Fangfang Zhang and Yuye Zhang and Paula Casanovas and
Jessica Schattschneider and Seumas P. Walker and
Bing Xue and Mengjie Zhang and Jane E. Symonds",
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title = "Health prediction for king salmon via evolutionary
machine learning with genetic programming",
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journal = "Journal of the Royal Society of New Zealand",
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note = "Latest Articles",
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keywords = "genetic algorithms, genetic programming, Evolutionary
machine learning, king salmon, health prediction,
classification",
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ISSN = "0303-6758",
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URL = "https://www.tandfonline.com/doi/full/10.1080/03036758.2024.2329228",
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DOI = "doi:10.1080/03036758.2024.2329228",
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size = "26 pages",
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abstract = "King (Chinook) salmon is the only salmon species
farmed in Aotearoa New Zealand and accounts for over
half of the world's production of king salmon.
Determining the health status of king salmon
effectively is important for farming. However, it is a
challenging task due to the complex biotic and abiotic
factors that influence health. Evolutionary machine
learning algorithms have shown their superiority in
learning models for challenging tasks. However, they
have not been investigated for health prediction in
king salmon farming. This paper focuses on data
processing and machine learning algorithm design to
develop king salmon health prediction models in
Aotearoa New Zealand. Particularly, this paper proposes
a king salmon health prediction method based on genetic
programming which is an evolutionary machine learning
algorithm. The results show that genetic programming
achieves the best overall performance among all
examined typical machine learning algorithms for most
trials. Further analyses show that genetic programming
can automatically detect important features for
learning classifiers for king salmon health
classification tasks effectively, and can also learn
potentially interpretable models. Our results are an
important step forward in developing health prediction
tools to automatically assess health status of farmed
king salmon in Aotearoa New Zealand.",
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notes = "Centre for Data Science and Artificial Intelligence &
School of Engineering and Computer Science, Victoria
University of Wellington, Wellington, New Zealand",
- }
Genetic Programming entries for
Fangfang Zhang
Yuye Zhang
Paula Casanovas
Jessica Schattschneider
Seumas P Walker
Bing Xue
Mengjie Zhang
Jane E Symonds
Citations