Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

By Pedram Ghamisi, Jon Atli Benediktsson. IEEE Geoscience and Remote Sensing Letters, 2015

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Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering (2017, methodology): “This algorithm is successfully used in different domains such as power flow analysis, text clustering, bioinformatics, wireless sensor networks, feature selection, and data clustering [13].”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, background): “Ghamisi and Benediktsson [116] proposed a new feature selection approach based on the integration of a GA and PSO.”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “trajan [52] 2009 DPSO-mutation-crossover Document Clustering Abdel Kader [53] 2010 GAI-PSO Data Clustering Kuo and Hong [54] 2013 HGP1, HGP2 Investment Portfolio Optimization Ghamisi and Benedictsson [55] 2015 GA-PSO Feature Selection Benvidi et al [56] 2016 GA-PSO Spectrophotometric determination o”
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles (2015, methodology): “In [61], a new FS technique was proposed, which is based on the integration of GA and PSO.”