Add to ORKGAbstract—Function optimisation is a major challenge in com-puter science. The No Free Lunch theorems state that if all functions with the same histogram are assumed to be equally probable then no algorithm outperforms any other in expectation. We argue against the uniform assumption and suggest a universal prior exists for which there is a free lunch, but where no particular class of functions is favoured over another. We also prove upper and lower bounds on the size of the free lunch. I
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Function optimisation is a major challenge in com-puter science. The No Free Lunch theorems state th...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
[...] 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...
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 theorems ” essentially say that for any two algorithms A and B, there are “as man...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...
Function optimisation is a major challenge in com-puter science. The No Free Lunch theorems state th...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
International audienceThis paper analyses extensions of No-Free-Lunch (NFL) theorems to countably in...
It is often claimed that Evolutionary Algorithms are superior to other optimization techniques, in p...
[...] 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...
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 theorems ” essentially say that for any two algorithms A and B, there are “as man...
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in alg...
AbstractThe No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimiz...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to des...
The classic NFL theorems are invariably cast in terms of single objective optimization problems. We ...