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.
Cited by¶
4 citations across 4 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.
- In machine learning, the No-Free-Lunch theorems rule out universal superiority across all 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.
- 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.
- 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.
- 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.
Mechanisms¶
- No-Universal-Winner Claim Review
- It is the governance embodiment of the No Free Lunch result — that no method is best across all problems, so any "best" must carry a reference class.
This sourceShows that any algorithm’s superior performance on one class of optimization problems is offset by poorer performance on another when averaged over all problems.
- It is the governance embodiment of the No Free Lunch result — that no method is best across all problems, so any "best" must carry a reference class.
Verification¶
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