Multiobjective Firefly Algorithm for Variable Selection in Multivariate Calibration

Firefly Algorithm is a newly proposed method with potential application on several real world problems, such as variable selection problem. This paper presents a Multiobjective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The main objective is to propose an opt...

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Bibliographic Details
Published inProgress in Artificial Intelligence Vol. 9273; pp. 274 - 279
Main Authors de Paula, Lauro Cássio Martins, da Silva Soares, Anderson
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
3D graphics & modelling
Artificial intelligence
Firefly algorithm
Information retrieval
Multiobjective optimization
Multivariate calibration
Variable selection
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Summary:Firefly Algorithm is a newly proposed method with potential application on several real world problems, such as variable selection problem. This paper presents a Multiobjective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The main objective is to propose an optimization to reduce the error value prediction of the property of interest, as well as reducing the number of variables selected. Based on the results obtained, it is possible to demonstrate that our proposal may be a viable alternative in order to deal with conflicting objective-functions. Additionally, we compare MOFA with traditional algorithms for variable selection and show that it is a more relevant contribution for the variable selection problem.
ISBN:9783319234847
3319234846
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-23485-4_27