Parallel Evolutionary Algorithms for Feature Selection in High Dimensional Datasets

Feature selection in high-dimensional datasets is con-sidered 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