Particle swarm optimization algorithm and its applications: a systematic review
Throughout the centuries, nature has been a source of inspiration, with much still to learn
from and discover about. Among many others, Swarm Intelligence (SI), a substantial branch …
from and discover about. Among many others, Swarm Intelligence (SI), a substantial branch …
A survey on evolutionary machine learning
Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …
function like humans. AI has been applied to many real-world applications. Machine …
Greylag goose optimization: nature-inspired optimization algorithm
Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …
order to create new and powerful competing algorithms. Such algorithms can be divided into …
A high-dimensional feature selection method based on modified Gray Wolf Optimization
H Pan, S Chen, H **ong - Applied Soft Computing, 2023 - Elsevier
For data mining tasks on high-dimensional data, feature selection is a necessary pre-
processing stage that plays an important role in removing redundant or irrelevant features …
processing stage that plays an important role in removing redundant or irrelevant features …
Boosted binary Harris hawks optimizer and feature selection
Y Zhang, R Liu, X Wang, H Chen, C Li - Engineering with Computers, 2021 - Springer
Feature selection is a required preprocess stage in most of the data mining tasks. This paper
presents an improved Harris hawks optimization (HHO) to find high-quality solutions for …
presents an improved Harris hawks optimization (HHO) to find high-quality solutions for …
A survey on swarm intelligence approaches to feature selection in data mining
One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …
causes the issue of “the curse of dimensionality” when applying machine learning …
Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Many fields such as data science, data mining suffered from the rapid growth of data volume
and high data dimensionality. The main problems which are faced by these fields include …
and high data dimensionality. The main problems which are faced by these fields include …
Feature selection based on artificial bee colony and gradient boosting decision tree
H Rao, X Shi, AK Rodrigue, J Feng, Y **a… - Applied Soft …, 2019 - Elsevier
Data from many real-world applications can be high dimensional and features of such data
are usually highly redundant. Identifying informative features has become an important step …
are usually highly redundant. Identifying informative features has become an important step …
An evolutionary algorithm for large-scale sparse multiobjective optimization problems
In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …
(MOPs) have been extensively investigated in the evolutionary computation community …
A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier
COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early
detection of (COVID-19) patients is essential for disease cure and control. There is a vital …
detection of (COVID-19) patients is essential for disease cure and control. There is a vital …