# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 4 Analysis and Discussion
The solution model may change over time because there is no one technique that can be used to solve every problem. After investigating the abovementioned works on hybrid evolutionary algorithms for FS in classification, we are able to list out the following advantages of hybridization. * Efficiency of the base algorithm can be improved (P1 [64], P4 [67], P8 [71], P11 [74], P14 [77], P20 [9], P22 [83], P25 [86], P26 [87], P28 [89], P35 [17]). * Premature convergence and the local optimum trap issue can be addressed (P2 [65], P5 [68], P6 [69], P11 [74], P15 [78], P31 [92], P32 [93], P33 [94], P34 [95], P35 [17]). * Balance between both exploration and exploitation can be maintained (P3 [66], P7 [70], P8 [71], P19 [81], P29 [90]). * The poor exploitation capability of some of the base methods can be improved (P9 [72], P21 [82], P23 [84], P30 [91], P35). * The optimal solution identified in each iteration can be enhanced (P10 [73], P12 [75], P16 [79], P18 [80]). * The searching procedure can converge to the best global solution (P12 [75], P13 [76], P17 [6], P17 [6], P24 [85], P27 [88]). Although the presented articles are able to improve the performance of the FS techniques, they still have some limitations that point in the direction of future research. * They are not verified with real-world applications like the biomedical domain (P1 [64], P3 [66], P8 [71], P10 [73], P12 [75], P13 [76], P15 [78], P16 [79], P31 [92], P32 [93]). * They are not tested with high-dimensional datasets (P1 [64], P3 [66], P4 [67], P6 [69], P7 [70], P8 [71], P9 [72], P10 [73], P11 [74], P12 [75], P13 [76], P15 [78], P16 [79], P18 [80], P19 [81], P24 [85], P27 [88], P29 [90], P32 [93], P33 [94]). * In some cases, the proposed algorithm is unable to find the global optimum (P3 [66], P14 [77], P27 [88]). * The fitness value focused only on the error rate and not on the number of features (P5 [68]). * They take longer to execute (P35 [17]). * The performance of the proposed approach is not compared with other existing hybrid approaches (P6 [69], P7 [70], P10 [73], P11 [74], P12 [75], P15 [78], P16 [79], P17 [6], P18 [80], P19 [81], P20 [9], P21 [82], P22 [83], P23 [84], P25 [86], P26 [87], P27 [88], P28 [89], P30 [91], P34 [95]). * They are verified with a few datasets (P14 [77], P17 [6], P20 [9], P21 [82]). As hybrid models for FS are becoming more effective and more efficient solutions, the following concerns have to be analysed by performing further enhancement. * The capability of newly developed methods has not been thoroughly explored, particularly in terms of their scalability, and therefore additional research is suggested for FS in high-dimensional real-world applications. * Since computation complexity is one of the key issues in most hybrid approaches for FS, it is recommended that more appropriate measures to reduce computational complexity should be proposed. Two key considerations must be weighed in order to do so: (1) more efficient ways to perform searching in the large solution spaces and (2) faster evaluation tools. * The FS priorities, such as computational burden and space complexity, can indeed be viewed in combination with the two main objectives of the hybrid FS problem (i.e., exploration and exploitation). * Proposing new methodologies that soften the fitness landscape will significantly reduce the problem's complexities and motivate the development of more effective search strategies. * Most of the existing studies in the literature used only one fitness function. However, FS can be viewed as an MO problem and thus, the application of hybridization in multiobjective FS tasks is an open research domain for researchers. * As hybrid FS techniques are time-consuming as compared to the others, employing parallel processing during the FS phase is also an area of research to be explored. * Most of the abovementioned articles are wrapper-based; however, the optimal solutions generated by wrapper approaches are less generic.