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Genetic Algorithms are a type of optimization and search algorithm inspired by the process of natural selection. They work by evolving a population of candidate solutions towards an optimal solution, with each iteration involving selection, crossover, and mutation operations. In the context of Computer Science and Algorithms, Genetic Algorithms can be used to find approximate solutions to complex optimization problems where traditional algorithms may not be feasible. They are particularly useful in situations where the problem space is too large or complex to be searched exhaustively, and where there is no clear path to an optimal solution. In this hierarchy, Genetic Algorithms fall under the broader category of Algorithms, which in turn falls under Computer Science and Science. They are a powerful tool for solving real-world problems, and their use has been applied in fields such as machine learning, artificial intelligence, and engineering design.