Optimization of Neural Network for Resource Allocation Using Genetic Algorithms

Abstract

Background: In this study, the optimal allocation calculation was adopted to determine the reliability of the neural network, as it is considered a mathematical model whose purpose is to process data and machine learning. This study contracts with the reliability of neural networks and finding the optimal  work  for them.

Materials and Methods: This research used a genetic algorithm with an exponential cost function to understand how the human brain works, taking into account the cost of each component. Simulating the human brain using genetic algorithms has been the subject of research by many researchers in recent decades.

Results: The results were excellent in terms of improving the neural network allocation and reliability, as all the network elements were taken into account and tested for their reliability well to provide better results in processing. The study was compared to several research papers that addressed reliability testing in other scientific and technological fields, and the comparison was more favorable in terms of numerical results.

Conclusion: the study's findings increased the neural network's allocation and dependability. The reliability of each network component is divided by the system optimization problem, which makes use of the most crucial location. Because it involves limitations on both human and material resources (the neural network's reliability).