其他摘要：Differential Evolution (DE) and Genetic Algorithms (GA) are efficient stochastic, populationbased, metaheuristics for global optimization, that are widely used in many different fields. In order to explore the qualities of each algorithm at different times of the search, we used the algorithms in an interleaved way, evaluating them on different constrained optimization test problems from the literature. With this it was possible to observe the behavior of the final algorithm, a hybrid one, and the extent to which it alters the moment and number of times each component algorithm is used. Furthermore it was possible to perform comparisons between the proposed hybrid and the models usually adopted for the original algorithms.
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