# A Selection Process for Genetic Algorithm Using Clustering Analysis
## 2 Literature Review  
There are many algorithms that have been proposed in literature to solve the clustering problems. Some relevant studies that have explored the problem of clustering using various approaches include evolutionary algorithms such as evolutionary programing [9], particle swarm optimization [10; 11; 12], ant colony algorithms [13; 14], artificial bee colony [15], simulated annealing [16; 17] and tabu search [18]. Conversely, there have been many attempts to use GAs to solve clustering applications [7; 19; 20; 21; 22; 23; 24; 25; 26; 27], [PERSON] and [PERSON] [21] proposed a GA approach to clustering. They tested the performance of the algorithm on synthetic and real-life data sets. The GA-\(k\)-means algorithm was used to search for the cluster centres which minimize the clustering metric, showing results significantly superior to those of the \(k\)-means algorithm. Another genetic algorithm approach, the genetic \(k\)-means algorithm, was presented by [PERSON] and [PERSON] [7]; they defined a mutation operator specific to clustering problems. Recently, a novel optimization algorithm was proposed by [PERSON] [28] referred to as K-MCI, inspired by the natural and social tendency of cohort individuals to learn from one another. Since the novelty of the proposed algorithm revolves around the notion of introducing clustering analysis in the selection stage of the genetic algorithm, this section will avoid a survey of clustering techniques. The reader is referred to [29; 30; 31] for detailed surveys of clustering algorithms. Consequently, in the remainder of this section, we will review the most relevant optimization algorithms that have introduced clustering analysis in one way or another. In the process of genetic differentiation, the population subdivided was discussed in the literature. For instance, the island model [32] divides the population into discrete finite races, between which some migration occurs. The hypothesis is that multiple subpopulations help preserve a better genetic diversity, since each island can potentially follow a different search trajectory through the search space. Various \"islands\" conserve some degree of independence and therefore explore different regions of the search space while sharing some information by migration. On the other hand, various niching methods have been introduced into GAs to promote the formation of stable sub-populations in neighborhood of optimal solutions [33]. There are many commonly adopted techniques, such as deterministic crowding [34], sharing [35], clearing [36] and dynamic niche clustering [37]. Standard and deterministic crowding both suffer from genetic drift. In sharing and clearing methods, prior knowledge about the fitness landscape is required to set the niche radius. The set of cluster numbers in dynamic niche clustering will largely affect the quality and quantity of optimal solutions. Many researchers have investigated evolutionary algorithms for dynamic optimization problems (DOPs) because EAs are fundamentally inspired from biological evolution, which is always subject to an ever-varying environment. From the literature on DOPs, the traditional approaches use the multi-population method to find the optimum solutions for multi-modal functions. The core notion is to divide the search space into different sub-spaces, and then separately search within these sub-spaces. The challenge with these multi-population methods (such as [38; 39; 40]), is how to choose an appropriate number of sub-populations to cover the entire search space. Three major difficulties arise using multi-population methods: how to guide the particles towards different promising sub-regions, how to define sub-regions and how many sub-populations are required. In order to overcome these questions, a clustering particle swarm optimizer (CPSO) was proposed in [38; 41]. In the CPSO algorithm, a proper number of sub-swarms which cover different local regions are created using a clustering method.