This paper proposes multi-objective genetic algorithm for the problem of variable selection in multivariate calibration. We consider the problem related to the classification of biodiesel samples to detect adulteration, Linear Discriminant Analysis classifier. The goal of the multi--objective algorithm is to reduce the dimensionality of the original set of variables; thus, the classification model can be less sensitive, providing a better generalization capacity. In particular, in this paper we adopted a version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) and compare it to a mono-objective Genetic Algorithm (GA) in terms of sensitivity in the presence of noise. Results show that the mono-objective selects 20 variables on average and presents an error rate of 14%. One the other hand, the multi-objective selects 7 variables and has an error rate of 11%. Consequently, we show that the multi-objective formulation provides classification models with lower sensitivity to the instrumental noise when compared to the mono-objetive formulation.
The multiprocessor task scheduling problem has received considerable attention over the last three decades. In this context, a wide range of studies focuses on the design of evolutionary algorithms. These papers deal with many topics, such as task characteristics, environmental heterogeneity, and optimization criteria. To classify the academic production in this research field, we present here a systematic literature review for the directed acyclic graph (DAG) scheduling, that is, when tasks are modeled through a directed acyclic graph. Based on the survey of 56 works, we provide a panorama about the last 30 years of research in this field. From the analyzes of the selected studies, we found a diversity of application domains and mapped their main contributions.
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