Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
A comprehensive survey: Whale Optimization Algorithm and its applications
FS Gharehchopogh, H Gholizadeh - Swarm and Evolutionary Computation, 2019 - Elsevier
Abstract Whale Optimization Algorithm (WOA) is an optimization algorithm developed by
Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the …
Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the …
Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly
applied to solve NP-hard problems such as feature selection. However, it and most of its …
applied to solve NP-hard problems such as feature selection. However, it and most of its …
A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection
M Abdel-Basset, D El-Shahat, I El-Henawy… - Expert Systems with …, 2020 - Elsevier
Because of their high dimensionality, dealing with large datasets can hinder the data mining
process. Thus, the feature selection is a pre-process mandatory phase for reducing the …
process. Thus, the feature selection is a pre-process mandatory phase for reducing the …
Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey
The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …
concise and informative feature subsets, which presents a challenging task for machine …
A review of the modification strategies of the nature inspired algorithms for feature selection problem
This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …
researchers to guide them when planning to develop new Nature-inspired Algorithms …
Ctoa: toward a chaotic-based tumbleweed optimization algorithm
Metaheuristic algorithms are an important area of research in artificial intelligence. The
tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm …
tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm …
A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem
M Sharma, P Kaur - Archives of Computational Methods in Engineering, 2021 - Springer
Meta-heuristics are problem-independent optimization techniques which provide an optimal
solution by exploring and exploiting the entire search space iteratively. These techniques …
solution by exploring and exploiting the entire search space iteratively. These techniques …
Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection
AA Ewees, RR Mostafa, RM Ghoniem… - Neural Computing and …, 2022 - Springer
Seagull optimization algorithm (SOA) is a recent bio-inspired technique utilized to improve
the constrained large-scale problems in low computational cost and quick convergence …
the constrained large-scale problems in low computational cost and quick convergence …
[HTML][HTML] A hybrid extreme learning machine model with lévy flight chaotic whale optimization algorithm for wind speed forecasting
Efficient and accurate prediction of renewable energy sources (RES) is an interminable
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …