## 1 Introduction
### GA Based Approaches  
The evolutionary optimization algorithm is classified as a genetic algorithm (GA). Darwinian evolution and survival of the fittest are the core concept of GA. The work flow of GA consists of three steps fitness, survival, and reproduction in an optimized manner [17]. The process of GA is detailed in the Figure 6.  
The general operations of genetic optimization algorithm are,  
* Step 1--Individuals initial population generation
* Step 2--Fitness value calculation for all the individuals  
Figure 5: Classification of earlier clustering algorithms.  
Figure 6: Process of genetic algorithm (GA).  
* Step 3--Repeat Fitness based individual's selection
* Individual's selection based on genetic operators
* New individual's fitness calculation
* Update the Current population
* Step 4--End [18]  
Several optimization algorithms are developed in earlier days to improve the performance of the network. Some of the genetic algorithm based researches are discussed in this section. In this paper the new hybrid variant of genetic algorithm (GA) is proposed namely adaptive range genetic algorithm (ARGA) which is used to make use of the sample space reduction technique of cohort intelligence (CI) algorithm. As the results of using this model the quality and robustness of the network is improved [19]. In this work, the behavior of genetic algorithms is explained using hyperplastic materials [20]. Genetic algorithm is mainly used for community detection. The author provides genetic encoding methods in this paper which is SGR for community detection as well as the crossover and mutation process are handled effectively [21].  
For large-scale optimizations based coevolutionary searches we need additional modifications in genetic algorithm. The author proposed a multi-agent real-coded genetic algorithm (MA\(-\)RCGA) for effective frequency exchange process of the network [22]. In the recent days to perform multiple heterogeneous tasks a new model based genetic algorithm is introduced which are a new fuzzy mixed-integer linear programming (FMILP) model and the interval-valued intuitionistic fuzzy entropy weight (IVIFEW) method [23]. For the long-term production planning, production scheduling is a complicated issue. As so to overcome this drawback the maximum flow algorithm (MFA) is combined with genetic algorithm [24]. A stepwise genetic algorithm (SGA) is developed next to improvise the crossover and mutation process in genetic algorithm [25]. In few machine learning based approaches genetic algorithm is used which is to improve the performance of light scattering process [26].  
In this paper CPU/FPGA heterogeneous architecture scheduling problem is studied and the author proposed two genetic algorithm based approaches to overcome this issue which are the MPSoC and makespan (minimize the schedule length) [27]. The process of economic optimization is proposed in this paper using genetic algorithm [28; 29]. In order to reduce the cost optimization issues hybrid genetic-gravitational search algorithm (HG-GSA) is developed. Hybrid crossover technique is the core idea of the algorithm which is mainly used to reduce the computation cost [30].