## 2 Literature Review  
Fuzzy cognitive maps, a soft computing technique based on a human reasoning approach, were proposed by [PERSON] in 1986 [10]. They were inspired from cognitive maps, proposed by [PERSON] in 1976 [11]. Fuzzy cognitive maps, unlike conventional cognitive maps, integrate the power of fuzzy logic into their framework. FCMs have proved to be a good tool for modelling complex systems due to their capability of addressing uncertainties and improving the dataset's accuracy. Recent years have seen a tremendous increase in research related to FCMs. In 2003, an FCM was used along with decision trees for urinary bladder grading [12]. A hybrid model of an FCM with neural networks was used for pattern classification in 2008 [13]. A new text categorization method based on a similar rough set and an FCM was proposed in 2008 [14]. An extension of an FCM to aid in decision making regarding pulmonary infections, known as the intuitionistic fuzzy cognitive map, was introduced by [PERSON] and [PERSON] [15] and considered the expert's hesitancy in decision making. An FCM combined with ensemble learning for autism identification problems was proposed by [PERSON] and [PERSON] in 2012 [16]. [PERSON] presented fuzzy grey cognitive maps for modelling uncertainties [17]. [PERSON] proposed dynamic random fuzzy cognitive maps (DRFCM) to model dynamic systems [18]. In 2013, particle swarm optimization and FCMs were used for autism classification [19]. [PERSON] et al. proposed a two-step learning process for FCM in which the first step was particle swarm optimization, and the second step was ant colony optimization [20]. A time-dependent FCM used for diagnosing pulmonary diseases was introduced by [PERSON] et al. [21], [PERSON] et al. developed the belief-degree-distributed fuzzy cognitive maps (BDD-FCMs), where causal connections were represented by a belief structure [22]. In 2018, a fuzzy cognitive map model utilising map-reduce was introduced, presenting a parallel fuzzy cognitive map approach [23]. [PERSON] et al. proposed a big-data-driven fuzzy cognitive map model to handle big datasets using a fuzzy cognitive map [24]. [PERSON] et al. proposed multilayer fuzzy cognitive maps to diagnose autism spectrum disorder [25]. A model for identifying the pattern of load distribution on the plantar muscle of the foot to detect a flat or cavus foot using fuzzy cognitive maps (FCMs) trained by a genetic algorithm (GA) against a multilayer perceptron neural network (MLPNN) was proposed in 2020 [26]. Community detection recognizes clusters of nodes that are more intimately connected to one another than they are to other nodes in the network. These connected nodes in networks are of crucial importance across different research domains, providing valuable insights [27]. In social network analysis, identifying communities helps uncover hidden social structures, such as groups of friends or communities of interest, facilitating targeted marketing, understanding information diffusion, and analysing online behaviour [28; 29]. In biology, community detection aids in uncovering functional modules in protein-protein interaction networks, shedding light on cellular processes, disease mechanisms, and potential drug targets [30]. In financial networks, community detection assists in identifying risk concentrations, systemic vulnerabilities, and contagion paths [31]. A multitude of techniques have been developed to identify communities within networks, such as the Louvain method [32], the Walktrap algorithm [33], the Newman-Girvan algorithm [34], simulated annealing [35], the random walk algorithm [36], influence-guided label propagation [37], IGLP-weighted-ensemble [37], etc. Genetic algorithms are one of the widely adopted techniques for the identification of the community structure of the network [38; 39; 40; 41; 42; 43]. It is essential to encode the chromosome when utilising genetic algorithms to solve problems. For community detection, several encoding methods have been employed.