Lauro C. M. de Paula

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable selection problem in multivariate calibration. The main drawback of this task lies in its computation burden, as it grows polynomially with the number of variables available. In this context, this paper proposes a GPU-based FA for variable selection in a multivariate calibration problem. Such implementation is aimed at improving the computational efficiency of the algorithm. For this purpose, a new strategy of regression coefficients calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving a large number of variables. In such example, gains of speedup were obtained. Additionally the authors also demonstrate that the FA, in comparison with traditional algorithms, can be a relevant contribution for the variable selection problem.

Feature Selection using Genetic Algorithm: An Analysis of the Bias-Property for One-Point Crossover

Genetic algorithms (GAs) have been used for feature selection with binary representation. Even if binary representation has perfect probability to include or remove a feature in the search process, some works in the field of chemometrics have reported criticism about a high number of features selected by GA implementations. Thus, in this paper, we aim to propose an investigation of the number of features selected on a point of view of the bias-property using implementations from the GA-PLS toolboxes (Genetic Algorithm with Partial Least Square). The study is performed using an one-point crossover operator and a common initialization procedure used in the matlab toolboxes. Results show the existence of such a bias that influences the increase in the number of features over the generations.

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 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.

Variable Selection for Multivariate Calibration in Chemometrics: A Real-World Application with Building Blocks Disruption Problem

Efficient algorithms have become an important focus in the variable selection problem to deal with larger and more complex datasets. Indeed, developing such algorithms is not an easy task since it is often necessary to adapt them into the problem context. Genetic algorithms (GAs) are an example of a selection method that has been used in multivariate calibration models. However, some works have emphasized that GAs usually cause the disruption of building blocks (BBs). This work comes from a doctoral thesis, which is still in development and aims to demonstrate theoretically and experimentally that GAs may be considered as an impracticable technique for variable selection in multivariate calibration problems, specially due to BBs disruption. Based on the preliminary results, we are able to claim that crossover operators should be avoided by causing the disruption of BBs in standard GAs, which leads to a poor variable selection performance.

Parallel regressions for variable selection using GPU

This paper proposes a parallel regression formulation to reduce the computational time of variable selection algorithms. The proposed strategy can be used for several forward algorithms in order to select uncorrelated variables that contribute for a better predictive capability of the model. Our demonstration of the proposed method include the use of Successive Projections Algorithm (SPA), which is an iterative forward technique that minimizes multicollinearity. SPA is traditionally used for variable selection in the context of multivariate calibration. Nevertheless, due to the need of calculating an inverse matrix for each insertion of a new variable in the model calibration, the computational performance of the algorithm may become impractical as the matrix size increases. Based on such limitation, this paper proposes a new strategy called Parallel Regressions (PR). PR strategy was implemented in the SPA to avoid the matrix inverse calculation of original SPA in order to increase the computational performance of the algorithm. It uses a parallel computing platform called Compute Unified Device Architecture (CUDA) in order to exploit a Graphics Processing Unit, and was called SPA-PR-CUDA. For this purpose, we used a case study involving a large data set of spectral variables. The results obtained with SPA-PR-CUDA presented 37

Variable selection as a non-completely decomposable problem: a case study in multivariate calibration

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Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

× times better performance compared to a traditional SPA implementation. Additionally, when compared to traditional algorithms we demonstrated that SPA-PR-CUDA may be a more viable choice for obtaining a model with a reduced prediction error value.