The No Free Lunch Theorem (NFLT) is a theoretical finding in optimization and machine learning. It states that:

  • A general-purpose, universal optimization strategy is impossible.
  • The only way one strategy can outperform another is if it is specialized to the structure of the specific problem under consideration.
  • When the performance of all optimization methods is averaged across all conceivable problems, they all perform equally well.
  • For certain types of mathematical problems, the computational cost of finding a solution, averaged over all problems in the class, is the same for any solution method.
  • No single machine learning algorithm is universally the best-performing algorithm for all problems.

The NFLT is often used in optimization and machine learning

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