Modelling the Transmission Dynamics of Obesity: A Multi-Objective Approach Using Dynamic Structured Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{parra:2023:GECCOcomp,
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author = "Daniel Parra and Alberto Gutierrez-Gallego and
Jose Manuel Velasco and Rafael-Jacinto Villanueva and
J. Ignacio Hidalgo",
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title = "Modelling the Transmission Dynamics of Obesity: A
{Multi-Objective} Approach Using Dynamic Structured
Grammatical Evolution",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Justyna Petke and Aniko Ekart",
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pages = "73--74",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, multi-objective optimization, obesity,
epidemiological techniques",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3596430",
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size = "2 pages",
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abstract = "Obesity is a significant public health concern that
affects millions of people worldwide. As a complex
condition influenced by various factors, modeling the
transmission dynamics of obesity is crucial for
developing effective interventions and public health
policies. In recent years, there has been a growing
interest in using epidemiological techniques to model
the spread of obesity within populations. However,
these techniques have limitations when accurately
describing the available data, especially when
considering uncertainties and inaccuracies. To address
this issue, this paper proposes a novel approach to
modeling the evolution of obesity as a transmission
disease. Specifically, we use multi-objective dynamic
structured grammatical evolution to generate models
that accurately describe available data while
considering their uncertainties and inaccuracies. Our
approach is based on a synthetic dataset that tracks
the evolution of a population divided into three groups
according to the Body Mass Index of its individuals.
Using this technique, we aim to identify lower-impact
relationships often overlooked in standard models,
which can improve the accuracy and understanding of the
transmission dynamics of obesity. Our work aims to
support the development of more effective public health
policies and interventions for reducing obesity.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Daniel Parra Rodriguez
Alberto Gutierrez-Gallego
Jose Manuel Velasco Cabo
Rafael-Jacinto Villanueva
Jose Ignacio Hidalgo Perez
Citations