## 2 Related Works  
The literature reveals that meta-heuristic algorithms have been very successful in tackling FS problems. GA and PSO algorithms have been utilized to develop effective FS methods for many problems. Several GA-based approaches have been proposed. Examples of these approaches are [37; 38; 39; 40; 41]. Moreover, many binary variants of PSO have been frequently applied in many FS methods. Some examples can be found in [PERSON] et al. [42], [PERSON] et al. [4], [PERSON] et al. [43], and [PERSON] et al. [44]. For instance, in [PERSON] et al. [42], an improved version of Binary PSO named Chaotic BPSO was used for FS in which two chaotic maps called logistic and tent were embedded in BPSO for estimating the value of inertia weight in the velocity equation of PSO algorithm. Another example is the recent work of [PERSON] et al. [43], where five strategies were used to update the value of the inertia weight parameter during the search process. The proposed approaches have shown better performance when compared to other similar FS approaches. ACO algorithm, which was introduced by [PERSON] et al. [45] was also applied in FS. As examples, one can refer to the work of [PERSON]. [46], [PERSON] et al. [47], and [PERSON] et al. [48]. Artificial Bee Colony (ABC) optimizer [49]. An example of using the ABC algorithm for FS is presented in [50]. In addition, as shown in [51], the binary version of the well-known meta-heuristic Bat Algorithm (BA) was used as an FS method. Experiential results demonstrated the superiority of BA based FS method in contrast with GA and PSO-based methods. In addition to the algorithms mentioned above that have been applied for FS, many recently introduced meta-heuristic algorithms such as Slap Swarm Algorithm (SSA) [6], Moth-Flame Optimization (MFO) [52], Dragonfly Algorithm (DA) [53], and Ant Lion Optimization (ALO) [54] have been successfully utilized in FS for many classification problems. Harris Hawks algorithm has been utilized to solve many optimization problems. For instance, as stated in [23], in the civil engineering domain, HHO was used to improve the performance of the artificial neural network classifier in predicting the soil slope stability. In addition, a hybrid model based on HHO and Differential Evaluation (DE) algorithms has been applied to tackle the task of color image segmentation. Using different measures for evaluation purposes, results prove that HHO-DE based approach is superior compared to several state-of-the-arts image segmentation techniques [24]. A novel automatic approach combining deep learning and optimization algorithms for nine control chart patterns (CCPs) recognition was proposed by [25]. An HHO algorithm was applied for the best tuning of ConvNet parameters. In addition, an improved version of the HHO algorithm that incorporates three strategies, including chaos, topological multi-population, and differential evolution (DE), was proposed by [26]. DE-driven multi-population HHO (CMDHHO) algorithm has shown its effectiveness in solving real-world optimization problems. The investigated literature reveals that some binary versions of HHO have been proposed since the appearance of the HHO algorithm in 2019 for FS problems [27; 28; 29; 55]. As presented in [27], a set of binary variants of the HHO algorithm was proposed as wrapper FS methods. Eight V-shaped and S-shaped TFs and four quadratic functions were used to transform the search space from continuous to binary. The performance of proposed variants of BHHO are compared with binary forms of different optimization algorithms, include DE algorithm, binary Flower Pollination Algorithm (FPA), binary Multi-Vorse Optimizer (MVO), binary SSA, and GA. The experimental results show that the QBHHO approach can mostly perform the best in terms of classification accuracy, least fitness value, and the lowest number of selected features.