Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …

For breast cancer diagnosis, computer-aided classification of histopathological images is of
critical importance for correct and early diagnosis. Transfer learning approaches for feature …

In this paper, we proposed a novel fitness-value-independent Differential Evolution to tackle
complex real-parameter single-objective optimization. There were three innovations in the …

The recently proposed swarm intelligence algorithm, Runge–Kutta Optimization (RUN), is
rooted in the fourth-order Runge–Kutta method. Compared with its counterparts, RUN …

Abstract Internet of Things (IoT) utilization is increasing every day in both industry and other
applications recently. However, the IoT communication is under security threats from …

To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search
for optimal solutions from zero initial information without taking advantage of the historical …

In recent years, feature selection research has quickly advanced to keep up with the age of
developing expert systems. This is because the applications of these systems sometimes …

This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted
optimization for airfoil shape optimization. The optimization problem is posed to maximize …

Datasets comprise a collection of features; however, not all of these features may be
necessary. Feature selection is the process of identifying the most relevant features while …

Indoor localization has broad application prospects, but accurately obtaining the location of
test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor …