Modelling the Transmission Dynamics of Obesity: A Multi-Objective Approach Using Dynamic Structured Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{parra:2023:GECCOcomp,

• author = "Daniel Parra and Alberto Gutierrez-Gallego and Jose Manuel Velasco and Rafael-Jacinto Villanueva and J. Ignacio Hidalgo",
• title = "Modelling the Transmission Dynamics of Obesity: A {Multi-Objective} Approach Using Dynamic Structured Grammatical Evolution",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Justyna Petke and Aniko Ekart",
• pages = "73--74",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, multi-objective optimization, obesity, epidemiological techniques",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596430",
• size = "2 pages",
• 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.",
• 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