Approximation Theory
Genetic algorithms are optimization techniques inspired by the process of natural selection, where potential solutions evolve over generations to solve complex problems. These algorithms use mechanisms such as selection, crossover, and mutation to create new generations of solutions, ultimately aiming to improve performance and efficiency in finding optimal results. They are particularly useful in scenarios where traditional methods may fall short, like in control systems and robotic applications.
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