Simulated Annealing is a probabilistic optimization technique inspired by the annealing process in metallurgy. It is used to find an approximate solution to an optimization problem by exploring the solution space and avoiding local minima. The algorithm starts at a high temperature, allowing for greater exploration, and gradually cools down, reducing the likelihood of accepting worse solutions over time. Common use cases include scheduling, routing, and various combinatorial optimization problems, where traditional methods may struggle to find optimal solutions efficiently.
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AI Fundamentals