## 3 Problem Statement
### Parallel GA for ANN Structure Synthesis  
The application of adaptive search algorithms for calculations demands significant computational resources, particularly in terms of speed and memory capacity. This requirement can complicate the effective implementation of GAs on traditional von Neumann computers [46]. To address these challenges and accelerate problem-solving, the use of parallel genetic algorithms (PGAs) in conjunction with DCSs is highly recommended [47].  
When parallelizing a GA, several factors influence the chosen strategy [48]. These include the method of evaluating suitability and implementing mutation, the choice between using a single population or multiple subpopulations, and the mechanism for exchanging individuals between subpopulations when applicable. Moreover, the choice of selection mechanism--global selection, which ranks and chooses individuals across the entire population, versus local selection, which confines competition to neighborhood or subpopulation subsets--critically affects selection pressure, convergence speed, maintenance of genetic diversity, and synchronization overhead in parallel implementations.  
Based on how these factors are implemented, various techniques for the parallelization of GAs emerge. PGAs can be broadly classified into several categories [49]. One category involves PGAs with distributed suitability assessment, also known as the Master-Slave PGA model. Another includes multipopulation PGAs, which incorporate coarse-grained algorithms and the island model. PGAs that leverage massive parallelism, referred to as fine-grained algorithms, form another category, while PGAs with dynamic subpopulations introduce additional flexibility. Moreover, PGAs can be classified as either stationary or unsteady, depending on the stability of subpopulations over time. Hybrid approaches integrate static island topologies with periodic migration of elite individuals and localized fitness evaluations, thereby balancing exploration and exploitation and enhancing resilience to premature convergence. [PERSON] et al. [50] systematically survey these and other GA parallelization techniques for multi-criteria optimization, detailing models such as master-slave, island, and fine-grained frameworks. Moreover, [PERSON] et al. [51] classify evolutionary algorithms according to their initialization strategies--random seeding, structured composition, and general-purpose heuristics--providing a comprehensive taxonomy for tailoring population startup to problem characteristics.  
Based on this analysis, the multi-population GA was selected as the method to address the complex computational problem. Consequently, training an ANN involves a multivariate optimization aimed at identifying the set of ANN weights that best minimize errors. A parallel GA is implemented to tackle this issue. As a result, the optimal ANN structure can be approached as a multi-parameter optimization problem, similar to tuning the parameters of the ANN. Recent years have demonstrated that GAs are one of the most effective methods for solving such problems.  
However, the application of PGAs demands considerable computational power. Often, cost-effective hardware solutions for server equipment are employed. In this research, we propose the development of a DSS to facilitate the selection of an efficient CN structure. ThisDSS would be applicable in enterprises where efficient CN structure selection is needed, with a focus on creating optimal computing infrastructure based on the equipment available at the enterprise. The main goal of implementing this system is to optimize resource utilization and enhance the efficiency of the computing infrastructure. The solution is proposed to be integrated during the design phase of the enterprise, enabling the optimal use of available hardware.