Optimal design of computer network reliability based on genetic algorithm

In modern computer networks, reliability is a key factor to ensure the efficient operation of the system. With the increasing complexity of network topology, it is difficult to effectively solve the problem of network reliability by using traditional optimization methods. This paper presents a new solution for computer network reliability optimization design based on genetic algorithm. Genetic algorithms simulate natural selection and genetic mechanisms and quickly search a wide range of solution Spaces to find the best network configuration through selection, crossover, mutation and other operations. The optimization design process includes several key steps: First, we need to define the network topology and reliability index, including connectivity, fault recovery ability and redundancy; Secondly, the initial population is generated using appropriate representation methods such as adjacency matrix or adjacency list. Then, the performance of each individual under different fault conditions was calculated through fitness evaluation. Next, select, cross, and mutate operations are performed to generate the next generation of individuals, continuously iterating to optimize network performance. The optimization results show that the design based on genetic algorithm can effectively improve the overall reliability of the network, shorten the fault recovery time, and enhance the stability of the network in the case of node failure or link interruption. This method not only has strong adaptability and global search ability, but also can deal with complex multi-objective optimization problems. The optimal design of network reliability based on genetic algorithm provides an effective solution for network security and stability, and provides a new research and application direction for future network optimization.