The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algorithms and has successfully drawn attention to theoretical foundation of optimization and search. However, we find several limitations in the original NFL paper. In this work, using results from the nature of search algorithms, we enhance several aspects of the original NFL Theorem. We have identified the properties of deterministic and probabilistic algorithms. We also provide an enumeration proof of the theorem. In addition, we show that the NFL Theorem is still valid for more general performance measures. This work serves as an application of the nature of search algorithms. ©2009 IEEE.published_or_final_versionThe 2009 IEEE International C...
International audienceThis paper investigates extensions of No Free Lunch (NFL) theorems to countabl...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
AbstractThe No Free Lunch (NFL) theorem due to Wolpert and Macready (IEEE Trans. Evol. Comput. 1(1) ...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
The No Free Lunch (NFL)theorem due to Wolpert and Macready (1997)has led to controversial discussion...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
International audienceThis paper investigates extensions of No Free Lunch (NFL) theorems to countabl...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
AbstractThe No Free Lunch (NFL) theorem due to Wolpert and Macready (IEEE Trans. Evol. Comput. 1(1) ...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
The No Free Lunch (NFL)theorem due to Wolpert and Macready (1997)has led to controversial discussion...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
International audienceThis paper investigates extensions of No Free Lunch (NFL) theorems to countabl...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...