# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey  
_Search Procedure_  
We adhere to the PRISMA principles for systematic reviews in our work (www.prismatstatement.org (accessed on 19 March 2023)). The relevant research questions are developed in accordance with these standards:  
1. What are the search approaches that were utilised to find the best features? 2. What are the search algorithms utilised to choose the best features for classification? 3. What hybrid search approaches have been utilised to choose the best characteristics for classification? The review began by searching for relevant research on Internet sites and in the University Teknologi PETRONAS online library. The Internet search was guided by the use of search engines to explore the electronic libraries and databases depicted in Figure 2. The terms \"hybrid + feature selection\", \"hybrid + search technique + feature selection\", and \"hybrid + search technique + feature selection + classification\" were the search parameters employed. There have been several studies on hybrid evolutionary FS. To ensure that the search was concentrated and controllable, the following inclusion and exclusion criteria were defined to select the publications for further study:  
* Inclusion Criteria:
* Research articles on hybrid evolutionary FS must have been published between 2009 and 2022. * Only research that has been published in peer-reviewed publications is included. * If the study had been published in more than one journal, we select the most complete version for inclusion. * Only related works utilised for classification are included. * Exclusion Criteria:
* Research articles prior to 2009 are not included. * Papers that are unrelated to the search topic are rejected. * Only items written in English are considered. Other languages are removed. The papers chosen by the abovementioned search procedure were reviewed by title and abstract in accordance with the inclusion and exclusion criteria. Then, all of the studies identified as relevant to our topic were downloaded for information extraction and additional investigation. Figure 2 provides information on the number of research studies discovered during the search of the most popular computerised libraries and databases. Figure 2: Number of papers identified. The next step was to prescreen the abstracts of the returning results. The primary goal of prescreening was to eliminate redundant data (some papers were returned in multiple databases) as well as incorrect findings. Improper findings were found in some studies where authors claimed to have employed the hybrid idea, but our research demonstrated that they hybridize filter and wrapper criteria rather than multiple search techniques. Finally, studies of 35 publications on hybrid metaheuristic approaches that were presented between 2009 and 2022 are covered in this review report. Figure 3 presents the number of papers collected for each year. All identified articles were scrutinized by their title and abstract. The current review paper provides a thorough picture on the metaheuristics used for hybridization and also presents a range of various classifiers and datasets, the application fields of the corresponding techniques, their objective/fitness functions and assessment metrics, and the application fields of various hybridised approaches, in contrast to individual methods. In Table 2, a brief introduction is given about each one of the collected papers in the relevant literature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Year** & **Aim** & **Experimental Evaluation** & **Assessment Metrics** \\ \hline P1 [64] & 2022 & The intention of this article is to design a simplified and functional hybrid algorithm for FS by considering the simplicity of the WOA and the stochastic nature of HHO. & Experimental findings on 18 benchmark datasets reveal that the proposed hybrid method is capable of enhancing the achievement of the conventional WOA concerning ACC, selected feature count, and execution time. \\ \hline \hline \end{tabular}
\end{table}