There have been many algorithms created and introduced in the literature inspired by
various events observable in nature, such as evolutionary phenomena, the actions of social …

Several feature selection methods have been developed to extract the optimal features from
a dataset in medical datasets classification. Creating an efficient technique has become a …

Feature selection (FS) is a popular data pre-processing technique in machine learning to
extract the optimal features to maintain or increase the classification accuracy of the dataset …

The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic
algorithm inspired by the dynamic mass balance on a control volume. However, EO may …

Equilibrium optimizer (EO) is a recent optimization method inspired by the physical equation
of the mass balance that provides the conservation of mass entering, leaving, and …

This study proposes a binary version of the recently developed Equilibrium Optimizer (EO)
widely used in various applications. The performance of the proposed Binary Equilibrium …

With the rapid expansion in Biological Sciences, biomedical data classification has become
challenging. These datasets generally consist of missing values, redundant features and …

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML)
tasks, aimed at removing uncorrelated and redundant attributes to enhance classification …

In the domains of data mining and machine learning, feature selection (FS) is an essential
preprocessing step that has a significant effect on the machine learning model's …

This work proposes a novel adaptive global optimization algorithm called Disturbance
Inspired Equilibrium Optimizer. The purpose of this study is to enhance the exploitation …