Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

In this paper, a novel swarm‐based metaheuristic algorithm is proposed, which is called
tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative …

Numerous optimization problems can be addressed using metaheuristics instead of
deterministic and heuristic approaches. This study proposes a novel population-based …

This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

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 …

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle
Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses …

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the
cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce …

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to
deal with Feature Selection (FS) problems. FS is an important data reduction step in data …

Disease identification and classification relies on Feature Selection (FS) to find the relevant
features for accurate medical diagnosis. FS is an optimization problem solved with the help …

Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …