American Journal of Computer Science and Engineering Survey (AJCSES) is a peer review open access journal publishing the state of the art research in computer science and engineering survey.
 

Feature selection in high-dimensional datasets is considered to be a complex and time-consuming problem. To enhance the accuracy of classification and reduce the execution time, Parallel Evolutionary Algorithms (PEAs) can be used. In this paper, we make a review for the most recent works which handle the use of PEAs for feature selection in large datasets.

We have classified the algorithms in these papers into four main classes (Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Scattered Search (SS), and Ant Colony Optimization (ACO)). The accuracy is adopted as a measure to compare the e ciency of these PEAs.

It is noticeable that the Parallel Genetic Algorithms (PGAs) are the most suitable algorithms for feature selection in large datasets; since they achieve the highest accuracy. On the other hand, we found that the Parallel ACO is time-consuming and less ccurate comparing with other PEA.

American Journal of Computer Science and Engineering Survey (AJCSES) is devoted to the publication referred papers on cutting-edge research in all the scientific areas of Computer Engineering and novel insights into its technology.  Journal intends to provide its researchers, practitioners and academics the latest and remarkable researches made by different scientists and industrial experts by providing free access to the published articles.
 

Manuscripts elucidating research surveys, technical reports, overviews, latest innovations and advancements in applied computer science and allied sciences are solicited.

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Conducting the Review

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Maegan Smith

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American Journal of Computer Science and Engineering Survey (AJCSES)

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