Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

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 …

Feature selection (FS) has received significant attention since the use of a well-selected
subset of features may achieve better classification performance than that of full features in …

This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …

Feature selection is to reduce both the dimensionality of data and the classification error rate
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …

By identifying relevant features from the original data, feature selection methods can
maintain or improve the classification accuracy and reduce the dimensionality. Recently …

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial
systems. In this study, we propose a bi-population balancing multi-objective evolutionary …

To reduce the dimensionality of high-dimensional data and enhance its classification
accuracy, feature selection can be regarded as a multiobjective optimization problem that …

With the increase of the number of features and the sample size, existing feature selection
(FS) methods based on evolutionary optimization still face challenges such as the “curse of …