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Genetic Algorithms in the context of Computer Vision involve using evolutionary computation techniques to optimize solutions for computer vision tasks. These algorithms are inspired by natural selection and genetics, where a population of candidate solutions evolves over time through processes such as mutation, crossover, and selection. In the realm of Computer Vision, Genetic Algorithms can be used to tackle problems like object detection, image segmentation, and feature extraction. They are particularly useful when dealing with complex optimization problems that have multiple local optima, making them a valuable addition to the toolkit of deep learning techniques commonly employed in Computer Vision.