No Free Lunch Theorems for Optimization.

Wolpert, D. H., & Macready, W. G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.

Type

Journal article

Intellectual base

Primary research

Year

1997

DOI

10.1109/4235.585893

Link

https://doi.org/10.1109/4235.585893

Cited from

computer_science, mathematics

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Axiomatic Incompatibility
  • In machine learning, the No-Free-Lunch theorems rule out universal superiority across all problem distributions.

    This sourceProves no optimization algorithm outperforms any other averaged over all possible problem distributions.

• Higher Order Function
  • In legal theory this maps to principles constraining which lawmaking schemata produce genuine law; in machine learning, to the no-free-lunch and inductive-bias discussions.

    This sourceEstablishes that schema-level guarantees over a family of optimizers depend on assumptions about the problem class — the inductive-bias/no-free-lunch limit on reasoning about a whole rule family at once.

• No Free Lunch Theorem
  • In optimization and search, no general-purpose optimizer beats random search averaged over all loss landscapes, and the value of Bayesian, evolutionary, gradient-based, or annealing methods comes entirely from a match between their priors and the actual problem structure.

    This sourceProves that, averaged over all possible objective functions, all black-box optimization algorithms have identical performance; the canonical statement of the conservation result.

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