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Genetic Algorithms are a subset of Machine Learning Methods, specifically those inspired by the process of natural selection and evolution. They manipulate a population of candidate solutions to find an optimal one, using genetic operators such as mutation, crossover, and selection. Genetic Algorithms can be used for optimization problems in various fields, including Technology, Artificial Intelligence, and Artificial Intelligence Algorithms. They are particularly useful when the search space is vast or complex, as they can efficiently explore multiple possible solutions simultaneously. In this hierarchy, Genetic Algorithms contribute to the development of intelligent systems capable of evolving and adapting through simulation of natural evolution.