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Genetic algorithms are a type of optimization technique inspired by natural selection and genetics, used in technical education to teach machine learning principles. They involve creating an initial population of solutions, evaluating their fitness, selecting parents based on their fitness values, and applying genetic operators such as mutation and crossover to produce offspring with improved fitness. The process repeats until a satisfactory solution is found or a maximum number of generations has been reached. This iterative approach mimics the evolutionary process of survival of the fittest and can be applied to various real-world problems, making it an essential concept in machine learning education.