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An Improvement of Genetic Algorithm based on Dynamic Operators Rates Controlled by the Population Performance

: This work presents a hybrid approach of genetic algorithm with dynamic operators rates that adapt to the phases of the evolutionary process. The operator’s rates are controlled by the amplitude variation and standard deviation of the objective function. Besides, a new stopping criterion is presented to be used in conjunction with the proposed algorithm. The developed approach is tested with six optimization benchmark functions from the literature. The results are compared to the genetic algorithm with constant rates in terms of the number of function evaluations, the number of iterations, execution time and optimum solution analysis.