A Biased Random-key Genetic Algorithm with Local Search Applied to Unsupervised Clustering of Cultural Data

• A. H. Aono UNIFESP
• R. M. de Oliveira UEMG
• B. O. Franchi UNIFESP
• J. S. Nagai UNIFESP
• H. E. S. Paz UNIFESP
• A. A. Chaves UNIFESP
• C. B. Martins UNIFESP

DOI: https://doi.org/10.5753/kdmile.2018.27395

Resumo

The relationship between culture and development has come to occupy a prominent place in the present time. From this perspective, culture is commonly said to be a constructive axis of identities, and also an economic factor which generates wealth. Due to the ongoing need of implementing an institutional policy for culture at Federal University of São Paulo, it was created a culture and extension project with the objective to contribute with the cultural actions in the college campuses. Sociocultural data from the university students was collected and herein we present a different methodology of analyzing such kind of dataset. Using the metaheuristic optimization Biased Random-key Genetic Algorithm with local search, we have achieved positive results in the identification of cultural subunits in the university. Significant information to understand the students’ integration and different cultural habits was obtained and a new way of visualizing this cultural scenario is herein proposed.

Palavras-chave: metaheuristic, optimization, multivariate analyses, cultural profile

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