# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 1 Introduction
The chosen individuals are exposed to the intervention of genetic operators in order to produce new entities that comprise the next generation [13]. A plethora of variations of metaheuristic methods has already been developed to support the FS tasks. When defining a metaheuristic approach, exploration and exploitation are two opposing aspects to take into account. In order to increase the effectiveness of these algorithms, it is essential to establish a good balance between these two aspects. This is because the algorithms perform well in some situations but poorly in others. Every nature-inspired approach has advantages and disadvantages of its own; hence it is not always practical to predict which algorithm is best for a given situation [14]. Researchers [15] now face a hurdle in the implementation and high-precision suggestion of modern metaheuristics for real-world applications. As a result, several researchers are working to solve FS challenges by using hybrid metaheuristics. By merging and coordinating the exploration and exploitation processes, hybridization aims to identify compatible alternatives to ensure the best possible output of the applied optimization methods [16]. A typical strategy for addressing such issues is to combine the advantages of various independent architectures through the hybridization of metaheuristic methods [15; 17]. This review paper extends our previous work presented in [18]. The reasons for broadening and extending the research on hybrid FS are highlighted as follows. * The initial work [18] only focused and reviewed a limited number of papers (only 10 in number) published from 2020-2021. In order to provide a more comprehensive overview of the field, the additional relevant research on hybrid FS from 2009-2022 is extremely important to include in the review study. * The current review paper deepens the scope of our research on multiple domains covering a wide range of metaheuristic approaches and wrapper classifiers. * The literature review presented in the current paper aims to fulfill the highly evolving nature of research in the field of FS, and it is very important to stay up to date with the latest developments in order to provide the most accurate and relevant information to the readers. * Therefore, we believed it was important to design the current updated and extended review paper, which will be of interest to researchers in the FS domain. We intend to address research issues and challenges that are open and interesting in terms of further research, and to provide a thorough overview of hybrid evolutionary techniques used to solve FS problems. This review draws the attention of scholars working with various metaheuristic frameworks, enabling them to further investigate enlightened approaches for tackling the complex FS problems often encountered in big data applications across many application domains. The remaining parts of this review article include Section 2, which gives an outline of feature-collection processes and important contextual information. The details of the applied literature review on hybrid evolutionary methods for FS are presented in Section 3. Section 4 provides analysis and guidance for future research based on the literature studies. The last section contains the conclusions of this study. Table 1 summarizes the acronyms of all terms used in this paper (i.e., Table 1 presents the names of all presented FS selection methods, ML models, parameters, and corresponding evaluation metrics).