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 …

A large number of algorithms have been proposed for feature subset selection. Our
experimental results show that the sequential forward floating selection algorithm, proposed …

Practical pattern-classification and knowledge-discovery problems require the selection of a
subset of attributes or features to represent the patterns to be classified. The authors' …

Feature Selection (FS) is an important preprocessing step that is involved in machine
learning and data mining tasks for preparing data (especially high-dimensional data) by …

Pattern recognition generally requires that objects be described in terms of a set of
measurable features. The selection and quality of the features representing each pattern …

Feature subset selection (FSS) has received a great deal of attention in statistics, machine
learning, and data mining. Real world data analyzed by data mining algorithms can involve …

This study proposed a novel PSO–SVM model that hybridized the particle swarm
optimization (PSO) and support vector machines (SVM) to improve the classification …

Rapid developments in the field of genetic algorithms along with the popularity of the first
edition precipitated this completely revised, thoroughly updated second edition of The …

As genetic algorithms (GAs) become increasingly popular, they are applied to difficult
problems that may require considerable computations. In such cases, parallel …

Feature selection and feature extraction are the most important steps in classification
systems. Feature selection is commonly used to reduce dimensionality of datasets with tens …