Research on the Comprehensive Performance Evaluation Method of Genetic Algorithm Based on Euclidean Distance Minimization

In order to solve the problem of selecting optimal control parameters for various genetic operators in genetic algorithms, this article propose a comprehensive performance evaluation function based on minimizing Euclidean distance in three-dimensional space, Quantitatively evaluate the performance of algorithms with different control parameters by using comprehensive optimization instead of one or two aspects of optimization. The correctness of this method was verified through experiments of directly selecting proportional parameters through selection operators and determining the probability of crossover operators.