An improved genetic algorithm is proposed, which introduces an adaptive adjustment mechanism for sorting selection pressure to ensure that the selection pressure is dynamically adjusted as the population traits change, and a new competitive selection crossover strategy is used to improve the average performance of individuals in the population. Typical test functions are selected for simulation, and the results show that the algorithm has greatly improved the optimization accuracy and convergence speed compared with the original algorithm, and the convergence probability is over 90%. Keywords: genetic algorithm; sorting selection; crossover strategy
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