@Article{jcs.2021.017018,
AUTHOR = {Yu Xue, Asma Aouari, Romany F. Mansour, Shoubao Su},
TITLE = {A Hybrid Algorithm Based on PSO and GA for Feature Selection},
JOURNAL = {Journal of Cyber Security},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {117--124},
URL = {http://www.techscience.com/JCS/v3n2/43999},
ISSN = {2579-0064},
ABSTRACT = {One of the main problems of machine learning and data 
mining is to develop a basic model with a few features, to reduce the 
algorithms involved in classification’s computational complexity. In 
this paper, the collection of features has an essential importance in the 
classification process to be able minimize computational time, which 
decreases data size and increases the precision and effectiveness of 
specific machine learning activities. Due to its superiority to 
conventional optimization methods, several metaheuristics have been 
used to resolve FS issues. This is why hybrid metaheuristics help 
increase the search and convergence rate of the critical algorithms. A 
modern hybrid selection algorithm combining the two algorithms; the 
genetic algorithm (GA) and the Particle Swarm Optimization (PSO) to 
enhance search capabilities is developed in this paper. The efficacy of 
our proposed method is illustrated in a series of simulation phases, 
using the UCI learning array as a benchmark dataset.},
DOI = {10.32604/jcs.2021.017018}
}