The concept of "No Free Lunch" in the context of optimization and machine learning refers to a theorem that states there is no one-size-fits-all algorithm that performs the best across all possible problems. Essentially, an algorithm that performs well on one class of problems may perform poorly on another. This is particularly relevant in optimization, where it highlights the need for choosing algorithms tailored to specific problem domains.
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