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 …

In the era of healthcare and its related research fields, the dimensionality problem of high-
dimensional data is a massive challenge as it is crucial to identify significant genes while …

The “curse of dimensionality” and the high computational cost have still limited the
application of the evolutionary algorithm in high-dimensional feature selection (FS) …

Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …

Feature selection (FS) is a real-world problem that can be solved using optimization
techniques. These techniques proposed solutions to make a predictive model, which …

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction
problems, based on deep learning approaches. Predictive models are useful ploys in solar …

Various real-world applications can be formulated as feature selection problems, which
have been known to be NP-hard. In this paper, we propose an effective feature selection …

Feature selection (FS) in data with class imbalance or missing values has received much
attention from researchers due to their universality in real-world applications. However, for …

Feature selection enhances classification accuracy by removing irrelevant and redundant
feature. Feature selection plays an important role in data mining and pattern recognition. In …

Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …