## 1 Introduction  
Communication networks, computers, and artificial intelligence technologies provide a vast array of tools and techniques to improve efficiency. With the development of these technologies and tools, huge amounts of data are generated, stored, and utilized [1]. For example, a large number of IoT devices monitor, sense, and generate continuous data from the edge [2]. In addition, operators retain large amounts of historical user data in various transaction and computing platforms. Then again, the generation of rich media data such as short videos and motion pictures also makes the amount of data in the network grow exponentially [3]. Machine learning can effectively use these data and learn rules and patterns from them to help people make predictions and decisions. Machine learning algorithms have been successfully applied to various fields of life, such as medicine, materials science, and physics. Specifically, machine learning algorithms can extract features from various types of data and use the features to train models for classification, regression, and clustering operations [4]. Despite the advantages of the mentioned machine learning algorithms in terms of effectiveness, wide application, and malleability, there are still some challenges and urgent issues that machine learning algorithms need to address. First, the training of machine learning algorithms is a time-consuming, computationally intensive, and energy-intensive process, which can lead to limited applications of machine learning algorithms [5]. Second, the training datasets of machine learning algorithms are derived from the features they extract, which are mostly extracted using automated tools and human experience, and have many repetitive, meaningless, or even misleading features [6]. These features can slow down the training process of machine learning algorithms even more and reduce the effectiveness of classification, clustering, and regression of machine learning algorithms. Therefore, the elimination of these useless and redundant features is important to improve the performance of machine learning algorithms and reduce their training consumption. Feature selection is an effective means to solve the above problem. Feature selection can eliminate useless and redundant features in the dataset, thus reducing the number of features and improving the classification accuracy of machine learning algorithms. For a dataset with \(N\) features, there are \(2^{N}\) feature selection schemes available, producing a combinatorial explosion. Therefore, selecting a subset of features with high classification accuracy and a low number of features can be regarded as an optimization problem. On the other hand, the feature selection problem is also proved to be an NP-hard problem. It is important to select and propose a suitable algorithm to solve the feature selection problem. In general, feature selection methods can be classified into three categories, namely filter-based methods, wrapper-based methods, and embedded methods. Specifically, filter-based methods use a statistical measure that gives a score to the relevance of each feature in the dataset, by which the importance of the feature can be quantified. Subsequently, the decision-maker can set a threshold to remove features with scores below the threshold, thus achieving a reduction in the number of features. However, such methods do not consider the complementarity and mutual exclusivity among features, and therefore the classification accuracy obtained by the subset of features selected by such methods is low [7]. Embedded-based methods are a special kind of wrapper-based method, so they are not discussed in this paper. The wrapper-based methods introduce classifiers and learning algorithms. The learning algorithm continuously generates new feature subsets, while the classifier evaluates the generated feature subsets and selects the optimal one in continuous iterations [8].