All basic concepts have been introduced so understand the GeneticFuzzy systems and facilitate the understanding the proposed methodology. The necessary steps to implement the adaptive genetic algorithm based on fuzzy rules was outlined, in which the crossover and mutation probabilities are changed based on a Mamdani fuzzy inference system, to improve the diversity of the population of the genetic algorithm. It has demonstrated from computational results that the proposed methodology presents a better performance than an ordinary genetic algorith. This is because it always adapt the genetic operators to find the global optimal solution without diminishing the diversity of the population. This is very important once the diversity be lost the algorithm can converge to local optimal solution. It was analyzed as vary the values of crossover and mutation probabilities, shown that these probabilities varied in a symmetrical form, and it is possible to change this property through the fuzzy system parameters. This methodology is able to modify its properties in adaptive form, and can work with complex multimodal functions, resulting in a sufficiently robust method to optimize a variety of applications.