Feature selection via a novel chaotic crow search algorithm

By Gehad Ismail Sayed, Aboul Ella Otifey Hassanien, Ahmad Taher Azar. Neural computing & applications (Print), 2017

434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/3b685abf13ddfad4c77c9831a23001a0079fe77c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Priyanka Anand
2. Sankalap Arora
3. Gehad Ismail Sayed
4. Ghada Khoriba
5. Mohamed Hassan Haggag
6. Tansel Dökeroğlu
7. Ayça Deniz
8. Hakan Ezgi Kiziloz

In Biomedical Engineering

1. Sankalap Arora
2. Priyanka Anand

Citations in the corpus

• Chaotic grasshopper optimization algorithm for global optimization (2019, methodology): “These efficient and robust algorithms are utilized to tackle various problems such as economic dispatch optimization [8], design of an IIR filter [9], node localization in wireless sensor networks [10], feature selection [11] and neural networks [12].”
• A novel chaotic salp swarm algorithm for global optimization and feature selection (2018, background): “These related algorithms are artificial bee colony (ABC) [45], moth flame optimization (MFO) [28], particle swarm optimization (PSO) [46], chicken swarm optimization (CSO) [47], gray wolf optimizer (GWO) [48], bird swarm algorithm [49], crow search algorithm [7], whale optimization algorithm [50] an”