Algoritmo evolutivo multi-objetivo de tabelas para seleção de variáveis em calibração multivariada

• Autor(a) principal: Jorge, Carlos Antônio Campos
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/001300000czfk
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/3813
• topic: Seleção de variáveis; Algoritmos evolutivos; Calibração; Algoritmos multi-objetivos; Multivariate calibration; Variable selection; Evolutionary algorithms; Multi-objective algorithms; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

This work proposes the use of a multi-objective evolutionary algorithm that makes use of subsets stored in a data structure called table in which the best individuals from each objective considered are preserved. This approach is compared in this work with the traditional mono-objective evolutionary algorithm (GA), classical algorithms (PLS and SPA) and another classic multi-objective algorithm (NSGA-II). As a case study, a multivariate calibration problem is presented which involves the prediction of protein concentration in samples of whole wheat from the spectrophotometric measurements. The results showed that the proposed formulation has a smaller prediction error when compared to the mono-objective formulation and with a lower number of variables. Finally,astudyofnoisesensitivityobtainedbythemulti-objectiveformulationshoweda better resultwhen compared tothe other classical algorithmforvariable selection.