# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 2 Related Work
However, EC approaches often entail considerable computational costs because they typically include a wide range of assessments. The stability of an EC approach is also a critical concern, as the respective algorithms often pick different features from various rounds. Further research study is required as the growing number of characteristics in large-scale datasets also raises computational costs and decreases the consistency of EC algorithm application [13] in certain real-world FS activities. A high-level description of the most used EC algorithms is given below. * Genetic Algorithm (GA): A GA [46] is a metaheuristic influenced by natural selection that belongs to the larger class of evolutionary algorithms in computer science and operations research. GA relies on biologically inspired operators, such as mutation, crossover, and selection to develop high-quality solutions to optimization and search challenges. The GA is a mechanism that governs biological evolution and for tackling both constrained and unconstrained optimization issues. The GA adjusts a population of candidate solutions on a regular basis. * Particle Swarm Optimization (PSO): PSO is a bioinspired algorithm that is straightforward to use while looking for the best alternative in the solution space. It differs from other optimization techniques in that it simply requires the objective function and is unaffected by the gradient or any differential form of the objective. It also has a small number of hyperparameters. [PERSON] and [PERSON] proposed PSO in 1995 [47]. Sociobiologists think that a school of fish or a flock of birds moving in a group \"may profit from the experience of all other members\", as stated in the original publication. In other words, while a bird is flying around looking for food at random, all of the birds in the flock can share what they find and assist the entire flock to get the best hunt possible. While we may imitate the movement of a flock of birds, we can also assume that each bird is assisting us in locating the best solution in a high-dimensional solution space, with the flock's best solution being the best solution in the space. This is a heuristic approach because we can never be certain that the true global optimal solution exists, and it rarely does. However, we frequently discover that the PSO solution is very close to the global optimum. * Grey Wolf Optimizer (GWO): [PERSON] et al. [48] presented GWO as a new metaheuristic in 2014. The grey wolf's social order and hunting mechanisms inspired the algorithm. First, there are four wolves, or degrees of the social hierarchy, to consider when creating GWO. * The \(\alpha\) wolf: the solution having best fitness value;
* the \(\beta\) wolf: the solution having second-best fitness value;
* the \(\delta\) wolf: the solution having third-best fitness value; and
* the \(\omega\) wolf: all other solutions. As a result, the algorithm's hunting mechanism is guided by the first three appropriate wolves, \(\alpha\), \(\beta\), and \(\delta\). The remaining wolves are regarded as \(\omega\) and follow them. Grey wolves follow a set of well-defined steps during hunting: encircling, hunting, and attacking. * Harris Hawk Optimization (HHO): [PERSON] and his team introduced HHO as a new metaheuristic algorithm in 2019 [49]. HHO uses Harris hawk principles to investigate the prey, surprise bounce, and diverse assault techniques used by Harris hawks in the environment. Hawks reflect alternatives in HHO, whereas prey represents the best solution. The Harris hawks use their keen vision to follow the target and then conduct a surprise bounce to seize the prey they have spotted. In general, HHO is divided into two phases: exploitation and exploration. The HHO algorithm can be switched from exploration to exploitation, and the exploration behaviour can then be adjusted depending on the fleeing prey's energy. 2. Criteria for Evaluation: The common evaluation criteria for wrapper FS techniques are the classification efficiency and effectiveness by using the selected attributes.