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 recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA),
has manifested its capability in solving various optimization problems and many real-life …

Feature Selection (FS) is choosing a subcategory of features purposed to construct a
machine learning model. Among the copious existing FS algorithms, Binary Particle Swarm …

This article proposes a new hybrid swarm intelligence optimization algorithm called
monarch butterfly optimization (MBO) algorithm with cuckoo search (CS) algorithm, named …

For the past decades, practitioners and researchers have been fascinated by the job-shop
scheduling problems (JSSP) and have proposed many pristine meta-heuristic algorithms to …

The process of eliminating irrelevant, redundant and noisy features while trying to maintain
less information loss is known as a feature selection problem. Given the vast amount of the …

Background In the field of genomics and personalized medicine, it is a key issue to find
biomarkers directly related to the diagnosis of specific diseases from high-throughput gene …

Feature selection is one of the main issues in machine learning algorithms. In this paper, a
new binary hyper-heuristics feature ranks algorithm is designed to solve the feature …

Feature subset selection is considered as the most essential pre-processing step.
Metaheuristic approaches may be employed to discover a solution to difficulties in feature …

The selection of crossover and mutation procedures greatly influences the searchability,
convergence effectiveness, and precision of genetic algorithms. The dimensional crossover …