Towards modelling beef cattle management with Genetic Programming
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gp-bibliography.bib Revision:1.7954
- @Article{ABBONA:2020:LS,
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author = "Francesca Abbona and Leonardo Vanneschi and
Marco Bona and Mario Giacobini",
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title = "Towards modelling beef cattle management with Genetic
Programming",
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journal = "Livestock Science",
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year = "2020",
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volume = "241",
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pages = "104205",
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keywords = "genetic algorithms, genetic programming, Precision
livestock farming, Evolutionary algorithms, Machine
learning, Cattle breeding, Piemontese bovines",
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ISSN = "1871-1413",
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URL = "https://iris.unito.it/retrieve/e27ce430-63b3-2581-e053-d805fe0acbaa/Abbona2020_LS_OA.pdf",
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URL = "http://www.sciencedirect.com/science/article/pii/S1871141320302481",
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DOI = "doi:10.1016/j.livsci.2020.104205",
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abstract = "Among the Italian Piemontese Beef Breedings, the
yearly production of calves weaned per cow, that is the
calves that survive during the period of 60 days
following birth, is identified as the main target
expressing the performance of a farm. modeling farm
dynamics in order to predict the value of this
parameter is a possible solution to investigate and
highlight breeding strengths, and to find alternatives
to penalizing factors. The identification of such
variables is a complex but solvable task, since the
amount of recorded data among livestock is nowadays
huge and manageable through Machine Learning
techniques. Besides, the evaluation of the
effectiveness of the type of management allows the
breeder to consolidate the ongoing processes or, on the
contrary, to adopt new management strategies. To solve
this problem, we propose a Genetic Programming
approach, a white-box technique suitable for big data
management, and with an intrinsic ability to select
important variables, providing simple models. The most
frequent variables encapsulated in the models built by
Genetic Programming are highlighted, and their
zoological significance is investigated a posteriori,
evaluating the performance of the prediction models.
Moreover, two of the final expressions selected only
three variables among the 48 given in input, one of
which is the best performing among GP models. The
expressions were then analyzed in order to propose a
zootechnical interpretation of the equations.
Comparisons with other common techniques, including
also black-box methods, are performed, in order to
evaluate the performance of different type of methods
in terms of accuracy and generalization ability. The
approach entailed constructive and helpful
considerations to the addressed task, confirming its
key-role in the zootechnical field, especially in the
beef breeding management",
- }
Genetic Programming entries for
Francesca Abbona
Leonardo Vanneschi
Marco Bona
Mario Giacobini
Citations