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 …

The vast majority of today's data is collected and stored in enormous databases with a wide
range of characteristics that have little to do with the overarching goal concept. Feature …

One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Abstract Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently
developed and illustrates the emperor penguin's huddling behaviour. However, the original …

Because of their high dimensionality, dealing with large datasets can hinder the data mining
process. Thus, the feature selection is a pre-process mandatory phase for reducing the …

Feature selection (FS) is an important preprocessing technique for dimensionality reduction
in classification problems. Particle swarm optimization (PSO) algorithms have been widely …

Abstract The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that
was shown to have excellent performance for numerous optimization problems. In this …

The significant growth of modern technology and smart systems has left a massive
production of big data. Not only are the dimensional problems that face the big data, but …

For years, extensive research has been in the binarization of continuous metaheuristics for
solving binary-domain combinatorial problems. This paper is a continuation of a previous …

Text classification is one of the challenging computational tasks in machine learning
community due to the increased amounts of natural language text documents available in …