We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools related to No Free Lunch (NFL) where functions are restricted to some Benchmark (that need not be permutation closed), algorithms are restricted to some collection (that need not be permutation closed) or limited to some number of steps, or the performance measure is given. “Minimax distinctions” are considered from a geometric perspective, and basic results on performance matching are also presented.status: publishe
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. R...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
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 provides a fundamental limit governing all optimization/search algor...
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...
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such a...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
The No Free Lunch theorem (NFL) asks some serious questions to researchers interested in search pr...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. R...
We extend previous results concerning Black-Box search algorithms, presenting new theoretical tools ...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
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 provides a fundamental limit governing all optimization/search algor...
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...
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such a...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
The No Free Lunch theorem (NFL) asks some serious questions to researchers interested in search pr...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. R...