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        "description": "Finite search and cost-value sets, uniform all-function averaging and distinct oracle evaluations. Wall-clock runtime and arbitrary structured problem distributions are outside this equality. The time-dependent result has additional bijectivity restrictions. The 1997 optimization theorem is not a blanket statement that all learning algorithms perform equally in the real world.",
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      "statement": "# Wolpert and Macready: no free lunch theorems for optimization\n\nCould one search strategy be best for every optimization problem? Wolpert and Macready show why the answer changes when problems are averaged in a particular way. On finite search spaces, averaging uniformly over all objective functions removes the advantage of any strategy when performance depends on the values found after distinct queries. Practical success therefore depends on the structure of the problems encountered. This does not say that a useful optimizer, or a well-chosen benchmark, is impossible.\n\n## Scientific scope\n\nFinite search and cost-value sets, uniform all-function averaging and distinct oracle evaluations. Wall-clock runtime and arbitrary structured problem distributions are outside this equality. The time-dependent result has additional bijectivity restrictions. The 1997 optimization theorem is not a blanket statement that all learning algorithms perform equally in the real world.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nDavid H. Wolpert; William G. Macready\n\nSource-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.\n\n\n\n## Original sources\n\n- [Wolpert and Macready: original IEEE 1997 optimization paper, institutional copy](https://victoryepes.blogs.upv.es/wp-content/uploads/2020/10/No-free-lunch.pdf)\n",
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    "id": "wolpert-macready-no-free-lunch-1997",
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    "title": "Wolpert and Macready: no free lunch theorems for optimization",
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    "what_changed": "Could one search strategy be best for every optimization problem? Wolpert and Macready show why the answer changes when problems are averaged in a particular way. On finite search spaces, averaging uniformly over all objective functions removes the advantage of any strategy when performance depends on the values found after distinct queries. Practical success therefore depends on the structure of the problems encountered. This does not say that a useful optimizer, or a well-chosen benchmark, is impossible.",
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