Computer Software Maintenance and Optimization Based on Improved Genetic Algorithm

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Ming Lu



Optimizing computer software maintenance is the key goal, which also ensures dependable and consistent network performance. In order to increase genetic operations and evaluate the satisfaction and fitness index functions, this article employs an improved genetic algorithm. Utilizing the network's performance and controlling restrictions through controlled data iterations, the architecture is refined. The study also finds a link between the number of iterations and the rate of network optimization, supporting the results of the genetic algorithm. The results show that the reliability of the network system decreases as the number of genetic operation repeats increases. If a critical point is reached, the enhancement in network reliability tends to level off due to hardware constraints or other relevant factors. Notably, the study identifies the maximum attainable value of network reliability at 0.894, precisely at 100 iterations. These conclusions offer an essential framework for optimizing the design of computer network reliability, emphasizing the necessity of a well-balanced approach to genetic algorithm-based optimization.

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Special Issue - Next generation Pervasive Reconfigurable Computing for High Performance Real Time Applications