Using Genetic Programming to Identify Characteristics of Brazilian Regions in Relation to Rural Credit Allocation

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@Article{agriculture13050935,

• author = "Adolfo Vicente Araujo and Caroline Mota and Sajid Siraj",
• title = "Using Genetic Programming to Identify Characteristics of Brazilian Regions in Relation to Rural Credit Allocation",
• journal = "Agriculture",
• year = "2023",
• volume = "13",
• number = "5",
• pages = "article--number 935",
• month = "24 " # apr,
• keywords = "genetic algorithms, genetic programming, rural credit, criteria analysis, family farming, machine learning",
• ISSN = "2077-0472",
• URL = "https://www.mdpi.com/2077-0472/13/5/935",
• DOI = "doi:10.3390/agriculture13050935",
• size = "14 pages",
• abstract = "Rural credit policies have a strong impact on food production and food security. The attribution of credit policies to agricultural production is one of the main problems preventing the guarantee of agricultural expansion. In this work, we conduct family typology analysis applied to a set of research data to characterize different regions. Through genetic programming, a model was developed using user-defined terms to identify the importance and priority of each criterion used for each region. Access to credit results in economic growth and provides greater income for family farmers, as observed by the results obtained in the model for the Sul region. The Nordeste region indicates that the cost criterion is relevant, and according to previous studies, the Nordeste region has the highest number of family farming households and is also the region with the lowest economic growth. An important aspect discovered by this research is that the allocation of rural credit is not ideal. Another important aspect of the research is the challenge of capturing the degree of diversity across different regions, and the typology is limited in its ability to accurately represent all variations. Therefore, it was possible to characterize how credit is distributed across the country and the main factors that can influence access to credit.",
• notes = "Department of Industrial Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil",

}

Genetic Programming entries for Adolfo Vicente Araujo Caroline Maria de Miranda Mota Sajid Siraj

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