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
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract- The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the se...
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...
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...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such a...
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 classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract- The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the se...
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...
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...
This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' t...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
We show that all algorithms that search for an extremum of a cost function per-form exactly the same...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such a...
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 classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract- The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the se...