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No free lunch theorems for optimization

Datos Bibliográficos

ID23367199
AutoresDavid H Wolpert (0000-0003-3105-2869, IBM Research - Almaden), William G Macready (Santa Fe Institute)
Año1997
Volumen1
Número1
Páginas67-82
Fecha de publicación1997-04-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaIEEE Transactions on Evolutionary Computation (JOURNAL)
Identificadores de la revistaISSN: 1089-778X • E-ISSN: 1941-0026
EditorialInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/4235.585893
OpenAlexW2151554678
IdiomaEN
Citas recibidas67
Referencias citadas10

A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms.

A priori and a posteriori · Algorithm · Class (philosophy) · Continuous optimization · Interpretation (philosophy) · L-reduction · Mathematical optimization · Minimax · Multi-swarm optimization · Optimization problem · Advanced Bandit Algorithms Research · Advanced Optimization Algorithms Research · Artificial Intelligence · Computer Science · Mathematics · Metaheuristic Optimization Algorithms Research

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Obras citantes distintas67
Citas por año3,05
Intervalo de citas2004 - 2026 (23)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 62
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