The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussions on the usefulness of randomized search heuristics, in particular, evolutionary algorithms. Here a short and simple proof of the NFL theorem is given to show its elementary character. Moreover, the proof method leads to a generalization of the NFL theorem. Afterwards, realistic complexity theoretical based scenarios for black box optimization are presented and it is argued why NFL theorems are not possible in such situations. However, an Almost No Free Lunch (ANFL) theorem shows that for each function which can be optimized efficiently by a search heuristic there can be constructed many related functions where the same heuristic is bad. As...
The No Free Lunch theorem (NFL) asks some serious questions to researchers interested in search pr...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract — Recent work on the foundational underpinnings of black-box optimization has begun to unco...
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 discussion...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algor...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
SIGLEAvailable from TIB Hannover: RR 8071(2000,91)+a / FIZ - Fachinformationszzentrum Karlsruhe / TI...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
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 ...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
The No Free Lunch theorem (NFL) asks some serious questions to researchers interested in search pr...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract — Recent work on the foundational underpinnings of black-box optimization has begun to unco...
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 discussion...
Abstract — The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/s...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algor...
[...] Thus not only our reason fails us in the discovery of the ultimate connexion of causes and eff...
SIGLEAvailable from TIB Hannover: RR 8071(2000,91)+a / FIZ - Fachinformationszzentrum Karlsruhe / TI...
The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible...
The No Free Lunch (NFL) theorems for optimization tell us that when averaged over all possible optim...
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 ...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
The No Free Lunch theorem (NFL) asks some serious questions to researchers interested in search pr...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Abstract — Recent work on the foundational underpinnings of black-box optimization has begun to unco...